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SUMMARY:Coffee Break 
CREATED:20240313T100524Z
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URL:https://ets24.ewi.tudelft.nl/index.php/home/program/conference-program/coffee-break
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SUMMARY:Coffee Break - Scientific Posters 1
CREATED:20240313T100617Z
DTSTAMP:20240313T100617Z
URL:https://ets24.ewi.tudelft.nl/index.php/home/program/conference-program/coffee-break-scientific-posters-1
DESCRIPTION:\N \N  \N   Moderator:\N  \N  Theofilos Spyrou\N \N\N\N \N  \N   Affiliation:\N  \N  Delft University of Technology (NL)\N \N\N\N \N  \N   Formal Resilience Metric Characterization in Complex Digital Systems\N  \N  \N   \N    \N   \N  \N \N \N  \N   \N    Authors:\N   \N   Damiano Zuccala\N   \N    1, 2\N   \N   , Jean-Marc Daveau\N   \N    1\N   \N   , Philippe Roche\N   \N    1\N   \N   , Katell Morin-Allory\N   \N    2\N   \N  \N \N \N  \N   \N    Affiliations:\N   \N   \N    1\N   \N   STMicroelectronics (FR),\N   \N    2\N   \N   University Grenoble Alpes,CNRS (FR)\N  \N \N \N \N  \N   Abstract:\N  \N \N \N  \N   As digital systems are continuously becoming more complex, new methods are required to ensure their resilience. Research and industry are working together to develop automated formal methods, and, recently, great progress has been made to overcome this challenge. This work describes a general procedure to quantitatively determine, by Model Checking, the resilience level of a digital block whose flip-flops are perturbed by bit-flips. The flow relies on the formal proof, and provides a rich variety of results with much improved performance and accuracy (boost of ∼ 300x and ∼ 30x in the two test cases). The resilience metric is the number of distinct counterexamples provided by the formal engine, for each fault target. Failure traces are differentiated in two ways, showing on the test cases the great enhancement over simulation.\N  \N \N \N  \N   \N    Analyzing the Structural and Operational Impact of Faults in Floating-Point and Posit Arithmetic Cores for CNN Operations\N   \N   \N    \N     \N    \N   \N  \N  \N   \N    \N     Authors:\N    \N    Josie E. Rodriguez Condia, Juan-David Guerrero-Balaguera, Robert Limas Sierra, Matteo Sonza Reorda\N   \N  \N  \N   \N    \N     Affiliation:\N    \N    Politecnico di Torino (IT)\N   \N  \N  \N  \N   \N    Abstract:\N   \N  \N  \N   \N    This work reports a first attempt to evaluate the fine-grain impact of permanent faults in the structures of arithmetic hardware cores implementing two number formats (Posit and FP). We assess and analyze errors in the cores for two operations (Add, and Multiply), which are the most used in several modern applications, including machine learning. The results show that Posit cores are structurally more vulnerable to fault propagation and induce more output corruptions than FP cores (from 3.3% up to 6.2%). Moreover, we found that the average absolute error in faulty FP cores is higher by up to 2 orders of magnitude than in Posit ones.\N   \N  \N  \N   \N    \N     Hardening Bus-Encoders with Power-Aware Single Error Correcting Codes\N    \N    \N     \N      \N     \N    \N   \N   \N    \N     \N      Authors:\N     \N     Shlomo Engelberg\N     \N      1\N     \N     , Osnat Keren\N     \N      2\N     \N    \N   \N   \N    \N     \N      Affiliations:\N     \N     \N      1\N     \N     Jerusalem College of Technology (IL),\N     \N      2\N     \N     Bar-Ilan University (IL)\N    \N   \N   \N   \N    \N     Abstract:\N    \N   \N   \N    \N     Bus encoding is a technique for decreasing the power consumption of a chip by reducing the number of bit transitions during data transmission over a bus or during memory write operations. This paper introduces a structured technique for hardening bus-encoders to enable single error correction (SEC) while maintaining power awareness. The method is based on expurgating the Hamming code in a specific manner.\N    \N   \N   \N    \N     \N      MBIST-based weak bit screening method for embedded MRAM\N     \N     \N      \N       \N      \N     \N    \N    \N     \N      \N       Authors:\N      \N      Jongsin Yun\N      \N       1\N      \N      , Sina Bakhtavari Mamaghani\N      \N       2\N      \N      , Mehdi Tahoori\N      \N       2\N      \N      , Christopher Münch\N      \N       3\N      \N      , Martin Keim\N      \N       1\N      \N     \N    \N    \N     \N      \N       Affiliations:\N      \N      \N       1\N      \N      Siemens Digital Industries Software (US),\N      \N       2\N      \N      Karlsruhe Institute of Technology (DE),\N      \N       3\N      \N      Siemens Digital Industries Software (DE)\N     \N    \N    \N    \N     \N      Abstract:\N     \N    \N    \N     \N      Magnetoresistive random access memory (MRAM) is an attractive option to replace eFlash. The recent demonstration of a nano-second write speed and a 10e14 endurance are compelling performances even as an embedded MRAM for cache replacement. Both eFlash and cache applications often use large array sizes, which require tight defect control. The unique defects in MRAMs that are not easily detectable with traditional memory test algorithms can potentially cause test escapes. Test escapes will not only delay the manufacturing process but also cause reliability issues, which is fatal for safety-critical applications such as automotive. This paper presents effective ways of screening hard-to-find defects related to oxide surface quality. The devices with minor oxide degradation have properties in the grey zone, which spec out some of the properties, although they pass the functional test. We introduce a new test method to screen those spec out cells using read reference trimming.\N     \N    \N    \N     \N      \N       GNN-Based INC and IVC Co-optimization for Aging Mitigation\N      \N      \N       \N        \N       \N      \N     \N     \N      \N       \N        Authors:\N       \N       Yu-Guang Chen\N       \N        1\N       \N       , Hsiu-Yi Yang\N       \N        1\N       \N       , Ing-Chao Lin\N       \N        2\N       \N      \N     \N     \N      \N       \N        Affiliations:\N       \N       \N        1\N       \N       National Central University (TW),\N       \N        2\N       \N       National Cheng Kung University (TW)\N      \N     \N     \N     \N      \N       Abstract:\N      \N     \N     \N      \N       As semiconductor processes advance, circuit aging becomes prominent. One of the most severe aging effects is Negative Bias Temperature Instability (NBTI), which increases the threshold voltage and the propagation delay of PMOS transistors. To mitigate NBTI, aging mitigation methods such as Internal Node Control (INC) and Input Vector Control (IVC) have been proposed. INC applies designed logic gates, while IVC uses appropriate input patterns during circuit idle. However, INC leads to extra area overhead and power consumption, and the circuit structure limits the controllability of IVC. Although various approaches have proposed aging tolerance methods with INC or IVC, only a few of them consider co-optimization. In this paper, we introduce a GNN-based INC and IVC co-optimization framework to minimize aging-induced delay. The key concept of our framework is using GNN to identify serious-aged gates in a circuit, and then using INC and IVC to mitigate the aging effect under an area overhead constraint. The experimental results indicate that our method reduces aging-induced delay and area by 2.16 times and 29.5%, respectively, compared to previous work.\N      \N     \N     \N      \N       \N        Error Detection and Correction Codes for Safe In-Memory Computations\N       \N       \N        \N         \N        \N       \N      \N      \N       \N        \N         Authors:\N        \N        Luca Parrini\N        \N         1, 4\N        \N        , Taha Soliman\N        \N         1\N        \N        , Benjamin Hettwer\N        \N         1\N        \N        , Jan Micha Borrmann\N        \N         1\N        \N        , Simranjeet Singh\N        \N         2\N        \N        , Ankit Bende\N        \N         2\N        \N        , Vikas Rana\N        \N         2\N        \N        , Farhad Merchant\N        \N         3\N        \N        , Norbert Wehn\N        \N         4\N        \N       \N      \N      \N       \N        \N         Affiliations:\N        \N        \N         1\N        \N        Bosch Corporate Research,Robert Bosch GmbH (DE),\N        \N         2\N        \N        Forschungszentrum Ju ̈lich GmbH (DE),\N        \N         3\N        \N        Newcastle University (UK),\N        \N         4\N        \N        RPTU Kaiserslautern-Landau (DE)\N       \N      \N      \N      \N       \N        Abstract:\N       \N      \N      \N       \N        In-Memory Computing (IMC) introduces a new paradigm of computation that offers high efficiency in terms of latency and power consumption for AI accelerators. However, the non-idealities and defects of emerging technologies used in advanced IMC can severely degrade the accuracy of inferred Neural Networks (NN) and lead to malfunctions in safety-critical applications. In this paper, we investigate an architectural-level mitigation technique based on the coordinated action of multiple checksum codes, to detect and correct errors at run-time. This implementation demonstrates higher efficiency in recovering accuracy across different AI algorithms and technologies compared to more traditional methods such as Triple Modular Redundancy (TMR). The results show that several configurations of our implementation recover more than 91% of the original accuracy with less than half of the area required by TMR and less than 40% of latency overhead.\N       \N      \N      \N       \N        \N         Parallel-Check Trimming Test Approach for Selecting the Reference Resistance of STT-MRAMs\N        \N        \N         \N          \N         \N        \N       \N       \N        \N         \N          Authors:\N         \N         Pei-Yun Lin, Jin-Fu Li\N        \N       \N       \N        \N         \N          Affiliation:\N         \N         National Central University (TW)\N        \N       \N       \N       \N        \N         Abstract:\N        \N       \N       \N        \N         Spin-transfer-torque magnetic random access memory (STT-MRAM) is a candidate for next-generation memory to cope with scaling challenges of conventional memories. However, the STT-MRAM has a small on/off resistance ratio which poses challenges in designing the reference resistance. An effective approach to cope with this issue is to design a trimmable reference resistance that allows post-production control of the reference resistance. A trimming test should be used in the production test to find an appropriate reference resistance. In this paper, a parallel-check trimming test (PCTT) approach aimed at significantly reducing the trimming test time is proposed. In comparison with the existing binary-judge-based search test approach, the proposed PCTT approach drastically reduces trimming test time with nearly the same read yield.\N        \N       \N       \N        \N         \N          A Concept of Provably Detected Defects for Analog Defect Simulation Campaign Improvement\N         \N         \N          \N           \N          \N         \N        \N        \N         \N          \N           Authors:\N          \N          Vladimir A. Zivkovic\N          \N           1\N          \N          , Inga Abel\N          \N           2\N          \N          , Anthony Candage\N          \N           3\N          \N         \N        \N        \N         \N          \N           Affiliations:\N          \N          \N           1\N          \N          Siemens EDA (DK),\N          \N           2\N          \N          Infineon Technologies (DE),\N          \N           3\N          \N          Infineon Technologies (US)\N         \N        \N        \N        \N         \N          Abstract:\N         \N        \N        \N         \N          This paper introduces a concept of provably detected defects that are identified through topology analysis and AMS verification database inquiry. The main purpose is to associate detected status to such defect without simulation, thereby reducing the size of defect simulation campaign. The proposed approach is a combination of fundamental and empirical rule-based set targeting relevant steps that are part of regular design sign-off. The feasibility is illustrated on an industrial product that is currently undergoing final stages of the tape-out, with the results complying to the current status of upcoming IEEE P2427 standard in development.\N         \N        \N        \N         \N          \N           A Multi-Objective Evolutionary Approach for Test Network Design\N          \N          \N           \N            \N           \N          \N         \N         \N          \N           \N            Authors:\N           \N           Payam Habiby\N           \N            1\N           \N           , Fatemeh Shirinzadeh\N           \N            2\N           \N           , Sebastian Huhn\N           \N            3\N           \N           , Rolf Drechsler\N           \N            1, 2\N           \N          \N         \N         \N          \N           \N            Affiliations:\N           \N           \N            1\N           \N           University of Bremen (DE),\N           \N            2\N           \N           DFKI (DE),\N           \N            3\N           \N           Siemens Electronic Design Automation GmbH (DE)\N          \N         \N         \N         \N          \N           Abstract:\N          \N         \N         \N          \N           IEEE Std. 1687 (IJTAG) introduces reconfigurable scan networks that implement an effective test access in highly complex designs. Designing an optimized network, that provides access to the instruments, meets the non-functional constraints, and preserves a minimized routing effort, area overhead and test access time, forms a non-trivial optimization problem. This paper tackles the IJTAG network topology design challenge by proposing an evolutionary approach to synthesize reconfigurable scan networks with optimized routing and area overhead while minimizing the overall test time.\N          \N         \N         \N          \N           \N            AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators\N           \N           \N            \N             \N            \N           \N          \N          \N           \N            \N             Authors:\N            \N            Mahdi Taheri\N            \N             1\N            \N            , Natalia Cherezova\N            \N             1\N            \N            , Samira Nazari\N            \N             2\N            \N            , Ahsan Rafiq\N            \N             1\N            \N            , Ali Azarpeyvand\N            \N             1, 2\N            \N            , Tara Ghasempouri\N            \N             1\N            \N            , Masoud Daneshtalab\N            \N             1, 3\N            \N            , Jaan Raik\N            \N             1\N            \N            , Maksim Jenihhin\N            \N             1\N            \N           \N          \N          \N           \N            \N             Affiliations:\N            \N            \N             1\N            \N            Tallinn University of Technology (EE),\N            \N             2\N            \N            University of Zanjan (IR),\N            \N             3\N            \N            Malardalen University (SE)\N           \N          \N          \N          \N           \N            Abstract:\N           \N          \N          \N           \N            Multiplication is the most resource-hungry operation in the neural network’s processing elements. In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators. AdAM employs an adaptive adder relying on an unconventional use of the leading one position value of the inputs for fault detection through the optimization of unutilized adder resources. The proposed architecture uses a lightweight fault mitigation technique that sets the detected faulty bits to zero. The hardware resource utilization and the DNN accelerator’s reliability metrics are used to compare the proposed solution against the triple modular redundancy (TMR) in multiplication, unprotected exact multiplication, and unprotected approximate multiplication. It is demonstrated that the proposed architecture enables a multiplication with a reliability level close to the multipliers protected by TMR utilizing 63.54% less area and having 39.06% lower power-delay product compared to the exact multiplier.\N           \N          \N          \N           \N            \N             Training Large Language Models for System-Level Test Program Generation Targeting Non-functional Properties\N            \N            \N             \N              \N             \N            \N           \N           \N            \N             \N              Authors:\N             \N             Denis Schwachhofer\N             \N              1\N             \N             , Peter Domanski\N             \N              1\N             \N             , Steffen Becker\N             \N              1\N             \N             , Stefan Wagner\N             \N              1, 2\N             \N             , Matthias Sauer\N             \N              3\N             \N             , Dirk Pflüger\N             \N              1\N             \N             , Ilia Polian\N             \N              1\N             \N            \N           \N           \N            \N             \N              Affiliations:\N             \N             \N              1\N             \N             University of Stuttgart (DE),\N             \N              2\N             \N             Technical University of Munich (DE),\N             \N              3\N             \N             Advantest Europe (DE)\N            \N           \N           \N           \N            \N             Abstract:\N            \N           \N           \N            \N             System-Level Test (SLT) has been an integral part of integrated circuit test flows for over a decade and continues to be significant. Nevertheless, there is a lack of systematic approaches for generating test programs, specifically focusing on the non-functional aspects of the Device under Test (DUT). Currently, test engineers manually create test suites using commercially available software to simulate the end-user environment of the DUT. This process is challenging and laborious and does not assure adequate control over non-functional properties. This paper proposes to use Large Language Models (LLMs) for SLT program generation. We use a pre-trained LLM and fine-tune it to generate test programs that optimize non-functional properties of the DUT, e.g., instructions per cycle. Therefore, we use Gem5, a microarchitectural simulator, in conjunction with Reinforcement Learning-based training. Finally, we write a prompt to generate C code snippets that maximize the instructions per cycle of the given architecture. In addition, we apply hyperparameter optimization to achieve the best possible results in inference.\N            \N           \N           \N            \N             \N              A Fully Pipelined High-Performance Elliptic Curve Cryptography Processor for NIST P-256\N             \N             \N              \N               \N              \N             \N            \N            \N             \N              \N               Authors:\N              \N              Han Yan\N              \N               1, 2\N              \N              , Shuai Chen\N              \N               3\N              \N              , Junying Huang\N              \N               1, 2\N              \N              , Jing Ye\N              \N               1, 2, 4\N              \N              , Huawei Li\N              \N               1, 2, 4\N              \N              , Xiaowei Li\N              \N               1, 2\N              \N             \N            \N            \N             \N              \N               Affiliations:\N              \N              \N               1\N              \N              Institute of Computing Technology CAS (CN),\N              \N               2\N              \N              University of Chinese Academy of Sciences (CN),\N              \N               3\N              \N              Binary Semiconductor (CN)\N             \N            \N            \N            \N             \N              Abstract:\N             \N            \N            \N             \N              Elliptic curve cryptography (ECC) is widely used in public key encryption, but its high-speed deployment faces challenges due to algorithmic and arithmetic complexity. In this paper, we present a high-performance ECC processor for the elliptic curve point multiplication (ECPM) of NIST P-256. Our approach employs a fully pipelined architecture featuring a 7-stage, 256-bit multiplier operating at a high frequency. To manage the data flow of the ECPM operation process, we devise a controller equipped with configurable instructions, which provides ECPM operations with higher flexibility to meet diverse contextual requirements. Additionally, we introduce a compact pipeline schedule to reduce ECPM computation clock cycles. The proposed LUT-based design achieves ECPM computation in 0.039 ms on FPGA (Virtex-7 platform) and 0.037 ms on ASIC (90nm technology), requiring only 10712 clock cycles.\N             \N            \N           \N          \N         \N        \N       \N      \N     \N    \N   \N  \N \N\N
X-ALT-DESC;FMTTYPE=text/html:<div class="calendar-authors"> <p>  <b>   Moderator:  </b>  Theofilos Spyrou </p></div><div class="calendar-affiliations"> <p>  <b>   Affiliation:  </b>  Delft University of Technology (NL) </p></div><div class="calendar-item"> <div class="header-wrapper">  <h3 class="calendar-paperheader">   Formal Resilience Metric Characterization in Complex Digital Systems  </h3>  <div class="pdficon filter-red">   <a href="/index.php/download?filename=PS1-1.pdf" target="_blank">    <img src="/files/pdficon.svg"/>   </a>  </div> </div> <div class="calendar-authors">  <p>   <b>    Authors:   </b>   Damiano Zuccala   <sup>    1, 2   </sup>   , Jean-Marc Daveau   <sup>    1   </sup>   , Philippe Roche   <sup>    1   </sup>   , Katell Morin-Allory   <sup>    2   </sup>  </p> </div> <div class="calendar-affiliations">  <p>   <b>    Affiliations:   </b>   <sup>    1   </sup>   STMicroelectronics (FR),   <sup>    2   </sup>   University Grenoble Alpes,CNRS (FR)  </p> </div> <input class="abstract-toggle" id="abstract-toggle-2" type="checkbox"/> <label class="collapsible" for="abstract-toggle-2">  <b>   Abstract:  </b> </label> <div class="calendar-abstract abstract-content">  <p align="justify">   As digital systems are continuously becoming more complex, new methods are required to ensure their resilience. Research and industry are working together to develop automated formal methods, and, recently, great progress has been made to overcome this challenge. This work describes a general procedure to quantitatively determine, by Model Checking, the resilience level of a digital block whose flip-flops are perturbed by bit-flips. The flow relies on the formal proof, and provides a rich variety of results with much improved performance and accuracy (boost of ∼ 300x and ∼ 30x in the two test cases). The resilience metric is the number of distinct counterexamples provided by the formal engine, for each fault target. Failure traces are differentiated in two ways, showing on the test cases the great enhancement over simulation.  </p> </div> <div class="calendar-item">  <div class="header-wrapper">   <h3 class="calendar-paperheader">    Analyzing the Structural and Operational Impact of Faults in Floating-Point and Posit Arithmetic Cores for CNN Operations   </h3>   <div class="pdficon filter-red">    <a href="/index.php/download?filename=PS1-2.pdf" target="_blank">     <img src="/files/pdficon.svg"/>    </a>   </div>  </div>  <div class="calendar-authors">   <p>    <b>     Authors:    </b>    Josie E. Rodriguez Condia, Juan-David Guerrero-Balaguera, Robert Limas Sierra, Matteo Sonza Reorda   </p>  </div>  <div class="calendar-affiliations">   <p>    <b>     Affiliation:    </b>    Politecnico di Torino (IT)   </p>  </div>  <input class="abstract-toggle" id="abstract-toggle-3" type="checkbox"/>  <label class="collapsible" for="abstract-toggle-3">   <b>    Abstract:   </b>  </label>  <div class="calendar-abstract abstract-content">   <p align="justify">    This work reports a first attempt to evaluate the fine-grain impact of permanent faults in the structures of arithmetic hardware cores implementing two number formats (Posit and FP). We assess and analyze errors in the cores for two operations (Add, and Multiply), which are the most used in several modern applications, including machine learning. The results show that Posit cores are structurally more vulnerable to fault propagation and induce more output corruptions than FP cores (from 3.3% up to 6.2%). Moreover, we found that the average absolute error in faulty FP cores is higher by up to 2 orders of magnitude than in Posit ones.   </p>  </div>  <div class="calendar-item">   <div class="header-wrapper">    <h3 class="calendar-paperheader">     Hardening Bus-Encoders with Power-Aware Single Error Correcting Codes    </h3>    <div class="pdficon filter-red">     <a href="/index.php/download?filename=PS1-3.pdf" target="_blank">      <img src="/files/pdficon.svg"/>     </a>    </div>   </div>   <div class="calendar-authors">    <p>     <b>      Authors:     </b>     Shlomo Engelberg     <sup>      1     </sup>     , Osnat Keren     <sup>      2     </sup>    </p>   </div>   <div class="calendar-affiliations">    <p>     <b>      Affiliations:     </b>     <sup>      1     </sup>     Jerusalem College of Technology (IL),     <sup>      2     </sup>     Bar-Ilan University (IL)    </p>   </div>   <input class="abstract-toggle" id="abstract-toggle-4" type="checkbox"/>   <label class="collapsible" for="abstract-toggle-4">    <b>     Abstract:    </b>   </label>   <div class="calendar-abstract abstract-content">    <p align="justify">     Bus encoding is a technique for decreasing the power consumption of a chip by reducing the number of bit transitions during data transmission over a bus or during memory write operations. This paper introduces a structured technique for hardening bus-encoders to enable single error correction (SEC) while maintaining power awareness. The method is based on expurgating the Hamming code in a specific manner.    </p>   </div>   <div class="calendar-item">    <div class="header-wrapper">     <h3 class="calendar-paperheader">      MBIST-based weak bit screening method for embedded MRAM     </h3>     <div class="pdficon filter-red">      <a href="/index.php/download?filename=PS1-4.pdf" target="_blank">       <img src="/files/pdficon.svg"/>      </a>     </div>    </div>    <div class="calendar-authors">     <p>      <b>       Authors:      </b>      Jongsin Yun      <sup>       1      </sup>      , Sina Bakhtavari Mamaghani      <sup>       2      </sup>      , Mehdi Tahoori      <sup>       2      </sup>      , Christopher Münch      <sup>       3      </sup>      , Martin Keim      <sup>       1      </sup>     </p>    </div>    <div class="calendar-affiliations">     <p>      <b>       Affiliations:      </b>      <sup>       1      </sup>      Siemens Digital Industries Software (US),      <sup>       2      </sup>      Karlsruhe Institute of Technology (DE),      <sup>       3      </sup>      Siemens Digital Industries Software (DE)     </p>    </div>    <input class="abstract-toggle" id="abstract-toggle-5" type="checkbox"/>    <label class="collapsible" for="abstract-toggle-5">     <b>      Abstract:     </b>    </label>    <div class="calendar-abstract abstract-content">     <p align="justify">      Magnetoresistive random access memory (MRAM) is an attractive option to replace eFlash. The recent demonstration of a nano-second write speed and a 10e14 endurance are compelling performances even as an embedded MRAM for cache replacement. Both eFlash and cache applications often use large array sizes, which require tight defect control. The unique defects in MRAMs that are not easily detectable with traditional memory test algorithms can potentially cause test escapes. Test escapes will not only delay the manufacturing process but also cause reliability issues, which is fatal for safety-critical applications such as automotive. This paper presents effective ways of screening hard-to-find defects related to oxide surface quality. The devices with minor oxide degradation have properties in the grey zone, which spec out some of the properties, although they pass the functional test. We introduce a new test method to screen those spec out cells using read reference trimming.     </p>    </div>    <div class="calendar-item">     <div class="header-wrapper">      <h3 class="calendar-paperheader">       GNN-Based INC and IVC Co-optimization for Aging Mitigation      </h3>      <div class="pdficon filter-red">       <a href="/index.php/download?filename=PS1-5.pdf" target="_blank">        <img src="/files/pdficon.svg"/>       </a>      </div>     </div>     <div class="calendar-authors">      <p>       <b>        Authors:       </b>       Yu-Guang Chen       <sup>        1       </sup>       , Hsiu-Yi Yang       <sup>        1       </sup>       , Ing-Chao Lin       <sup>        2       </sup>      </p>     </div>     <div class="calendar-affiliations">      <p>       <b>        Affiliations:       </b>       <sup>        1       </sup>       National Central University (TW),       <sup>        2       </sup>       National Cheng Kung University (TW)      </p>     </div>     <input class="abstract-toggle" id="abstract-toggle-6" type="checkbox"/>     <label class="collapsible" for="abstract-toggle-6">      <b>       Abstract:      </b>     </label>     <div class="calendar-abstract abstract-content">      <p align="justify">       As semiconductor processes advance, circuit aging becomes prominent. One of the most severe aging effects is Negative Bias Temperature Instability (NBTI), which increases the threshold voltage and the propagation delay of PMOS transistors. To mitigate NBTI, aging mitigation methods such as Internal Node Control (INC) and Input Vector Control (IVC) have been proposed. INC applies designed logic gates, while IVC uses appropriate input patterns during circuit idle. However, INC leads to extra area overhead and power consumption, and the circuit structure limits the controllability of IVC. Although various approaches have proposed aging tolerance methods with INC or IVC, only a few of them consider co-optimization. In this paper, we introduce a GNN-based INC and IVC co-optimization framework to minimize aging-induced delay. The key concept of our framework is using GNN to identify serious-aged gates in a circuit, and then using INC and IVC to mitigate the aging effect under an area overhead constraint. The experimental results indicate that our method reduces aging-induced delay and area by 2.16 times and 29.5%, respectively, compared to previous work.      </p>     </div>     <div class="calendar-item">      <div class="header-wrapper">       <h3 class="calendar-paperheader">        Error Detection and Correction Codes for Safe In-Memory Computations       </h3>       <div class="pdficon filter-red">        <a href="/index.php/download?filename=PS1-6.pdf" target="_blank">         <img src="/files/pdficon.svg"/>        </a>       </div>      </div>      <div class="calendar-authors">       <p>        <b>         Authors:        </b>        Luca Parrini        <sup>         1, 4        </sup>        , Taha Soliman        <sup>         1        </sup>        , Benjamin Hettwer        <sup>         1        </sup>        , Jan Micha Borrmann        <sup>         1        </sup>        , Simranjeet Singh        <sup>         2        </sup>        , Ankit Bende        <sup>         2        </sup>        , Vikas Rana        <sup>         2        </sup>        , Farhad Merchant        <sup>         3        </sup>        , Norbert Wehn        <sup>         4        </sup>       </p>      </div>      <div class="calendar-affiliations">       <p>        <b>         Affiliations:        </b>        <sup>         1        </sup>        Bosch Corporate Research,Robert Bosch GmbH (DE),        <sup>         2        </sup>        Forschungszentrum Ju ̈lich GmbH (DE),        <sup>         3        </sup>        Newcastle University (UK),        <sup>         4        </sup>        RPTU Kaiserslautern-Landau (DE)       </p>      </div>      <input class="abstract-toggle" id="abstract-toggle-7" type="checkbox"/>      <label class="collapsible" for="abstract-toggle-7">       <b>        Abstract:       </b>      </label>      <div class="calendar-abstract abstract-content">       <p align="justify">        In-Memory Computing (IMC) introduces a new paradigm of computation that offers high efficiency in terms of latency and power consumption for AI accelerators. However, the non-idealities and defects of emerging technologies used in advanced IMC can severely degrade the accuracy of inferred Neural Networks (NN) and lead to malfunctions in safety-critical applications. In this paper, we investigate an architectural-level mitigation technique based on the coordinated action of multiple checksum codes, to detect and correct errors at run-time. This implementation demonstrates higher efficiency in recovering accuracy across different AI algorithms and technologies compared to more traditional methods such as Triple Modular Redundancy (TMR). The results show that several configurations of our implementation recover more than 91% of the original accuracy with less than half of the area required by TMR and less than 40% of latency overhead.       </p>      </div>      <div class="calendar-item">       <div class="header-wrapper">        <h3 class="calendar-paperheader">         Parallel-Check Trimming Test Approach for Selecting the Reference Resistance of STT-MRAMs        </h3>        <div class="pdficon filter-red">         <a href="/index.php/download?filename=PS1-8.pdf" target="_blank">          <img src="/files/pdficon.svg"/>         </a>        </div>       </div>       <div class="calendar-authors">        <p>         <b>          Authors:         </b>         Pei-Yun Lin, Jin-Fu Li        </p>       </div>       <div class="calendar-affiliations">        <p>         <b>          Affiliation:         </b>         National Central University (TW)        </p>       </div>       <input class="abstract-toggle" id="abstract-toggle-8" type="checkbox"/>       <label class="collapsible" for="abstract-toggle-8">        <b>         Abstract:        </b>       </label>       <div class="calendar-abstract abstract-content">        <p align="justify">         Spin-transfer-torque magnetic random access memory (STT-MRAM) is a candidate for next-generation memory to cope with scaling challenges of conventional memories. However, the STT-MRAM has a small on/off resistance ratio which poses challenges in designing the reference resistance. An effective approach to cope with this issue is to design a trimmable reference resistance that allows post-production control of the reference resistance. A trimming test should be used in the production test to find an appropriate reference resistance. In this paper, a parallel-check trimming test (PCTT) approach aimed at significantly reducing the trimming test time is proposed. In comparison with the existing binary-judge-based search test approach, the proposed PCTT approach drastically reduces trimming test time with nearly the same read yield.        </p>       </div>       <div class="calendar-item">        <div class="header-wrapper">         <h3 class="calendar-paperheader">          A Concept of Provably Detected Defects for Analog Defect Simulation Campaign Improvement         </h3>         <div class="pdficon filter-red">          <a href="/index.php/download?filename=PS1-9.pdf" target="_blank">           <img src="/files/pdficon.svg"/>          </a>         </div>        </div>        <div class="calendar-authors">         <p>          <b>           Authors:          </b>          Vladimir A. Zivkovic          <sup>           1          </sup>          , Inga Abel          <sup>           2          </sup>          , Anthony Candage          <sup>           3          </sup>         </p>        </div>        <div class="calendar-affiliations">         <p>          <b>           Affiliations:          </b>          <sup>           1          </sup>          Siemens EDA (DK),          <sup>           2          </sup>          Infineon Technologies (DE),          <sup>           3          </sup>          Infineon Technologies (US)         </p>        </div>        <input class="abstract-toggle" id="abstract-toggle-9" type="checkbox"/>        <label class="collapsible" for="abstract-toggle-9">         <b>          Abstract:         </b>        </label>        <div class="calendar-abstract abstract-content">         <p align="justify">          This paper introduces a concept of provably detected defects that are identified through topology analysis and AMS verification database inquiry. The main purpose is to associate detected status to such defect without simulation, thereby reducing the size of defect simulation campaign. The proposed approach is a combination of fundamental and empirical rule-based set targeting relevant steps that are part of regular design sign-off. The feasibility is illustrated on an industrial product that is currently undergoing final stages of the tape-out, with the results complying to the current status of upcoming IEEE P2427 standard in development.         </p>        </div>        <div class="calendar-item">         <div class="header-wrapper">          <h3 class="calendar-paperheader">           A Multi-Objective Evolutionary Approach for Test Network Design          </h3>          <div class="pdficon filter-red">           <a href="/index.php/download?filename=PS1-10.pdf" target="_blank">            <img src="/files/pdficon.svg"/>           </a>          </div>         </div>         <div class="calendar-authors">          <p>           <b>            Authors:           </b>           Payam Habiby           <sup>            1           </sup>           , Fatemeh Shirinzadeh           <sup>            2           </sup>           , Sebastian Huhn           <sup>            3           </sup>           , Rolf Drechsler           <sup>            1, 2           </sup>          </p>         </div>         <div class="calendar-affiliations">          <p>           <b>            Affiliations:           </b>           <sup>            1           </sup>           University of Bremen (DE),           <sup>            2           </sup>           DFKI (DE),           <sup>            3           </sup>           Siemens Electronic Design Automation GmbH (DE)          </p>         </div>         <input class="abstract-toggle" id="abstract-toggle-10" type="checkbox"/>         <label class="collapsible" for="abstract-toggle-10">          <b>           Abstract:          </b>         </label>         <div class="calendar-abstract abstract-content">          <p align="justify">           IEEE Std. 1687 (IJTAG) introduces reconfigurable scan networks that implement an effective test access in highly complex designs. Designing an optimized network, that provides access to the instruments, meets the non-functional constraints, and preserves a minimized routing effort, area overhead and test access time, forms a non-trivial optimization problem. This paper tackles the IJTAG network topology design challenge by proposing an evolutionary approach to synthesize reconfigurable scan networks with optimized routing and area overhead while minimizing the overall test time.          </p>         </div>         <div class="calendar-item">          <div class="header-wrapper">           <h3 class="calendar-paperheader">            AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators           </h3>           <div class="pdficon filter-red">            <a href="/index.php/download?filename=PS1-11.pdf" target="_blank">             <img src="/files/pdficon.svg"/>            </a>           </div>          </div>          <div class="calendar-authors">           <p>            <b>             Authors:            </b>            Mahdi Taheri            <sup>             1            </sup>            , Natalia Cherezova            <sup>             1            </sup>            , Samira Nazari            <sup>             2            </sup>            , Ahsan Rafiq            <sup>             1            </sup>            , Ali Azarpeyvand            <sup>             1, 2            </sup>            , Tara Ghasempouri            <sup>             1            </sup>            , Masoud Daneshtalab            <sup>             1, 3            </sup>            , Jaan Raik            <sup>             1            </sup>            , Maksim Jenihhin            <sup>             1            </sup>           </p>          </div>          <div class="calendar-affiliations">           <p>            <b>             Affiliations:            </b>            <sup>             1            </sup>            Tallinn University of Technology (EE),            <sup>             2            </sup>            University of Zanjan (IR),            <sup>             3            </sup>            Malardalen University (SE)           </p>          </div>          <input class="abstract-toggle" id="abstract-toggle-11" type="checkbox"/>          <label class="collapsible" for="abstract-toggle-11">           <b>            Abstract:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            Multiplication is the most resource-hungry operation in the neural network’s processing elements. In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators. AdAM employs an adaptive adder relying on an unconventional use of the leading one position value of the inputs for fault detection through the optimization of unutilized adder resources. The proposed architecture uses a lightweight fault mitigation technique that sets the detected faulty bits to zero. The hardware resource utilization and the DNN accelerator’s reliability metrics are used to compare the proposed solution against the triple modular redundancy (TMR) in multiplication, unprotected exact multiplication, and unprotected approximate multiplication. It is demonstrated that the proposed architecture enables a multiplication with a reliability level close to the multipliers protected by TMR utilizing 63.54% less area and having 39.06% lower power-delay product compared to the exact multiplier.           </p>          </div>          <div class="calendar-item">           <div class="header-wrapper">            <h3 class="calendar-paperheader">             Training Large Language Models for System-Level Test Program Generation Targeting Non-functional Properties            </h3>            <div class="pdficon filter-red">             <a href="/index.php/download?filename=PS1-12.pdf" target="_blank">              <img src="/files/pdficon.svg"/>             </a>            </div>           </div>           <div class="calendar-authors">            <p>             <b>              Authors:             </b>             Denis Schwachhofer             <sup>              1             </sup>             , Peter Domanski             <sup>              1             </sup>             , Steffen Becker             <sup>              1             </sup>             , Stefan Wagner             <sup>              1, 2             </sup>             , Matthias Sauer             <sup>              3             </sup>             , Dirk Pflüger             <sup>              1             </sup>             , Ilia Polian             <sup>              1             </sup>            </p>           </div>           <div class="calendar-affiliations">            <p>             <b>              Affiliations:             </b>             <sup>              1             </sup>             University of Stuttgart (DE),             <sup>              2             </sup>             Technical University of Munich (DE),             <sup>              3             </sup>             Advantest Europe (DE)            </p>           </div>           <input class="abstract-toggle" id="abstract-toggle-12" type="checkbox"/>           <label class="collapsible" for="abstract-toggle-12">            <b>             Abstract:            </b>           </label>           <div class="calendar-abstract abstract-content">            <p align="justify">             System-Level Test (SLT) has been an integral part of integrated circuit test flows for over a decade and continues to be significant. Nevertheless, there is a lack of systematic approaches for generating test programs, specifically focusing on the non-functional aspects of the Device under Test (DUT). Currently, test engineers manually create test suites using commercially available software to simulate the end-user environment of the DUT. This process is challenging and laborious and does not assure adequate control over non-functional properties. This paper proposes to use Large Language Models (LLMs) for SLT program generation. We use a pre-trained LLM and fine-tune it to generate test programs that optimize non-functional properties of the DUT, e.g., instructions per cycle. Therefore, we use Gem5, a microarchitectural simulator, in conjunction with Reinforcement Learning-based training. Finally, we write a prompt to generate C code snippets that maximize the instructions per cycle of the given architecture. In addition, we apply hyperparameter optimization to achieve the best possible results in inference.            </p>           </div>           <div class="calendar-item">            <div class="header-wrapper">             <h3 class="calendar-paperheader">              A Fully Pipelined High-Performance Elliptic Curve Cryptography Processor for NIST P-256             </h3>             <div class="pdficon filter-red">              <a href="/index.php/download?filename=PS1-7.pdf" target="_blank">               <img src="/files/pdficon.svg"/>              </a>             </div>            </div>            <div class="calendar-authors">             <p>              <b>               Authors:              </b>              Han Yan              <sup>               1, 2              </sup>              , Shuai Chen              <sup>               3              </sup>              , Junying Huang              <sup>               1, 2              </sup>              , Jing Ye              <sup>               1, 2, 4              </sup>              , Huawei Li              <sup>               1, 2, 4              </sup>              , Xiaowei Li              <sup>               1, 2              </sup>             </p>            </div>            <div class="calendar-affiliations">             <p>              <b>               Affiliations:              </b>              <sup>               1              </sup>              Institute of Computing Technology CAS (CN),              <sup>               2              </sup>              University of Chinese Academy of Sciences (CN),              <sup>               3              </sup>              Binary Semiconductor (CN)             </p>            </div>            <input class="abstract-toggle" id="abstract-toggle-13" type="checkbox"/>            <label class="collapsible" for="abstract-toggle-13">             <b>              Abstract:             </b>            </label>            <div class="calendar-abstract abstract-content">             <p align="justify">              Elliptic curve cryptography (ECC) is widely used in public key encryption, but its high-speed deployment faces challenges due to algorithmic and arithmetic complexity. In this paper, we present a high-performance ECC processor for the elliptic curve point multiplication (ECPM) of NIST P-256. Our approach employs a fully pipelined architecture featuring a 7-stage, 256-bit multiplier operating at a high frequency. To manage the data flow of the ECPM operation process, we devise a controller equipped with configurable instructions, which provides ECPM operations with higher flexibility to meet diverse contextual requirements. Additionally, we introduce a compact pipeline schedule to reduce ECPM computation clock cycles. The proposed LUT-based design achieves ECPM computation in 0.039 ms on FPGA (Virtex-7 platform) and 0.037 ms on ASIC (90nm technology), requiring only 10712 clock cycles.             </p>            </div>           </div>          </div>         </div>        </div>       </div>      </div>     </div>    </div>   </div>  </div> </div></div>
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SUMMARY:Coffee Break - PhD Forum 1 and Industry Posters
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DTSTAMP:20240313T101934Z
URL:https://ets24.ewi.tudelft.nl/index.php/home/program/conference-program/coffee-break-phd-forum-1-and-industry-posters
DESCRIPTION:\N \N  \N   Moderator:\N  \N  Paolo Rech\N \N\N\N \N  \N   Affiliation:\N  \N  Trento University (IT)\N \N\N\N \N  \N   On Parametrized Virtual Testing and Simulation of Verilog-AMS Behavioral Models\N  \N  \N   \N    \N   \N  \N \N \N  \N   \N    Authors:\N   \N   Thorben Schey\N   \N    1\N   \N   , Khaled Karoonlatifi\N   \N    2\N   \N   , Andrey Morozov\N   \N    1\N   \N   , Michael Weyrich\N   \N    1\N   \N  \N \N \N  \N   \N    Affiliations:\N   \N   \N    1\N   \N   University of Stuttgart Stuttgart (DE),\N   \N    2\N   \N   Advantest Europe GmbH (DE)\N  \N \N \N \N  \N   Abstract:\N  \N \N \N  \N   The traditional development of test programs for Analog Mixed Signal (AMS) circuits is both time-consuming and cost-intensive, with validation of only physically available chips. The testing is conducted by providing a set of test inputs. The type of inputs and the optimal sequence is highly dependent on the Circuit Under Test (CUT) and therefore requires expert knowledge. Hence, a desirable solution is a virtual test framework that enables pre-tapeout testing and aids the design phase with early feedback regarding the fulfillment of circuit specifications. In this extended abstract, we introduce a concept of a new simulative approach based on behavioral models featuring parameterizable fault characteristics in Verilog-AMS models. The proposed framework also encompasses models of tester instruments and signal transmission paths. Preliminary simulation results are provided showcasing the feasibility of this approach.\N  \N \N \N \N  \N   Biography:\N  \N \N \N  \N   Thorben Schey received the M.Sc. degree with distinction in electrical engineering and information technology from the University of Stuttgart, Germany, in 2023, where he is currently pursuing the Ph.D. degree at the Institute of Industrial Automation and Software Engineering (IAS). He is also a Doctoral Researcher at IAS. His research interests include virtual testing and behavioral models for chip test.\N  \N \N \N  \N   \N    Techniques for Building Reliable and Energy-Efficient Hardware Accelerators for Dynamic Deep Neural Networks\N   \N   \N    \N     \N    \N   \N  \N  \N   \N    \N     Authors:\N    \N    Rama Mounika Kodamanchili, Maksim Jenihhin\N   \N  \N  \N   \N    \N     Affiliation:\N    \N    Tallinn University of Technology (EE)\N   \N  \N  \N  \N   \N    Abstract:\N   \N  \N  \N   \N    This paper explores the design and integration of Dynamic Deep Neural Networks (D2NN or DynNN) into\Nhardware accelerators to improve reliability while minimizing energy consumption. To achieve optimal performance, we study the decision-making sub-networks, early-exiting techniques, and pruning strategies. Considering the increasing transistor density in AI chips, we address the critical issue of D2NN vulnerability to\Nradiation-induced soft errors and adversarial attacks, proposing architectural modifications and validation techniques to ensure the integrity of these accelerators for safety-critical applications.\N   \N  \N  \N  \N   \N    Biography:\N   \N  \N  \N   \N    I am a first-year Ph.D. student at Tallinn University of Technology, studying under the supervision of Prof. Dr. Maksim Jenihhin. My research focuses on the Reliability of Dynamic Neural Network (D2NN) Hardware accelerators. \N\NAdditionally, I am a full-time DFX Pre-Silicon Validation Engineer at Intel. I develop System Verilog-based UVM test cases for IP and sub-system validation. Furthermore, I conduct digital fault grading of HVM test cases before delivering them to post-silicon customers.\N   \N  \N  \N   \N    \N     Design of efficient Hardware Inference Engines for Edge AI\N    \N    \N     \N      \N     \N    \N   \N   \N    \N     \N      Authors:\N     \N     Ahsan Rafiq, Maksim Jenihhin\N    \N   \N   \N    \N     \N      Affiliation:\N     \N     Tallinn University of Technology (EE)\N    \N   \N   \N   \N    \N     Abstract:\N    \N   \N   \N    \N     Very recently, the rampant fusion of AI algorithms with computing hardware for edge AI devices has raised the\Nconcerns for designing the delay and power-area efficient, and reliable hardware architectures. The imperative motivation behind this work is to bridge the gap between increasing computational demands and the limitations of AI hardware. This PhD work aims to introduce novel approaches for advanced and optimized hardware architectures to improve the performance parameters of delay, power and area for efficient AI hardware.\N    \N   \N   \N   \N    \N     Biography:\N    \N   \N   \N    \N     Ahsan Rafiq, is an early stage researcher in Tallinn University of Technology since October 2023. He did his master thesis research in high speed and low power digital circuits and systems’ design. Therefore, it was my motivation earlier to work in this field due to my expertise and growing research in this field of digital system design. So, I have decided to pursue my career further in research and academia. This is my motivation for PhD.\N    \N   \N   \N    \N     \N      Deploying Compact and Dependable DNNs in Safety-critical Applications\N     \N     \N      \N       \N      \N     \N    \N    \N     \N      \N       Authors:\N      \N      Leonardo Alexandrino de Melo\N      \N       1\N      \N      , Alberto Bosio\N      \N       1, 2\N      \N      , Rodrigo Possamai Bastos\N      \N       2\N      \N     \N    \N    \N     \N      \N       Affiliations:\N      \N      \N       1\N      \N      Ecole Centrale de Lyon (FR),\N      \N       2\N      \N      Univ. Grenoble Alpes,CNRS,Grenoble (FR)\N     \N    \N    \N    \N     \N      Abstract:\N     \N    \N    \N     \N      This work aims at developing generation methods for compacting Deep Neural Networks (DNNs) to fit low-power micro controllers used in safety and mission-critical applications. The objective is to implement a Network Architecture Search (NAS) framework to identify the best DNN implementation for a set of application and system requirements, accounting for different embedded computers. To achieve this objective, we\Nintend to develop a bottom up approach in which we characterize DNN components (e.g., convolutional layer, fully connected layer) in terms of energy efficiency and reliability. In this paper, we target the reliability characterization of DNNs through radiation experiments. Thermal neutron beaming will reveal hardware error models that will be used to simulate assorted hardening techniques. After observing and comparing several DNN algo rithms, on different embedded hardware architectures, these combinations will be validated on new radiation tests. It is expected to understand specific weaknesses of each system and which hardening solutions can circumvent radiation effects on each hardware/software combination.\N     \N    \N    \N    \N     \N      Biography:\N     \N    \N    \N     \N      Leonardo Alexandrino is an Aerospace Engineer and Mechatronics Technician from Brazil, who started his PhD this year at Lyon Institute of Nanotechnology at Ecole Centrale de Lyon, in France. His topics of research are: Deep Neural Networks, Embedded Systems, Automation, Nanosatellites, Test and Systems Engineering.\N     \N    \N    \N     \N      \N       Towards Ultra-Reliable Automotive Systems-on-Chip\N      \N      \N       \N        \N       \N      \N     \N     \N      \N       \N        Author:\N       \N       Giusy Iaria\N      \N     \N     \N      \N       \N        Affiliation:\N       \N       Politecnico di Torino (IT)\N      \N     \N     \N     \N      \N       Abstract:\N      \N     \N     \N      \N       Embedded nano-electronic systems are becoming more prevalent in people’s daily lives. As a result, chip and\Nembedded system manufacturing has become increasingly complicated and huge in recent years. As a result, manufacturers are faced with significant complexity in testing their embedded systems to ensure the needed reliability for safety-critical fields. This Ph.D. thesis aims at introducing novel testing approaches\Nto face the complexity of new and more complex industrial case studies.\N      \N     \N     \N     \N      \N       Biography:\N      \N     \N     \N      \N       Giusy Iaria is a Ph.D. student in her third year in\NComputer and Control Engineering at Polytechnic\Nof Turin, Italy. She is part of the CAD & Reliability\Nresearch group with a strong focus on Embedded\Nelectronics testing. After a research fellowship on\Nthe reliability of safety-critical devices, she started\N her Ph.D. to find strategies and evaluation methods\N to reach ultra-reliability in Automotive Systems-on-Chip.\N      \N     \N     \N      \N       \N        System-Level Test techniques for Automotive SoCs\N       \N       \N        \N         \N        \N       \N      \N      \N       \N        \N         Author:\N        \N        Francesco Angione\N       \N      \N      \N       \N        \N         Affiliation:\N        \N        Politecnico di Torino (IT)\N       \N      \N      \N      \N       \N        Abstract:\N       \N      \N      \N       \N        No Abstract\N       \N      \N      \N      \N       \N        Biography:\N       \N      \N      \N       \N        Francesco Angione is a Computer Engineer with an M.Sc., Embedded Systems track, obtained from Politecnico di Torino in 2020. Currently, he is working towards a Ph.D. on "System-Level-Test techniques for Automotive SoCs" at Politecnico di Torino in the CAD & Reliability group. His main interests are real-time operating systems, computer architectures, and their dependability.\N       \N      \N      \N       \N        \N         Leveraging FPGAs for Faster and Less Memory-Demanding Burn-In Testing\N        \N        \N         \N          \N         \N        \N       \N       \N        \N         \N          Author:\N         \N         Tommaso Foscale\N        \N       \N       \N        \N         \N          Affiliation:\N         \N         Politecnico di Torino (IT)\N        \N       \N       \N       \N        \N         Abstract:\N        \N       \N       \N        \N         In recent years, we have seen exponential growth in the complexity of SoCs. More powerful and efficient chips have been developed and integrated into more complex and increasingly interconnected environments. However, this increase in complexity has translated into an increase in the difficulty of testing the correct functioning of these chips. Techniques used in the past have proven insufficient to cope with this increase in complexity. Today, more than ever, it is necessary to find new testing techniques or modify existing techniques to guarantee the correct functioning of the chips once they have passed the testing phase. In this article, we will discuss how, through a board equipped with an FPGA, it is possible to implement a fast burn-in test to ensure the quality of a device, which was carried out using an STMicroelectronics chip from the SPC58 family.\N        \N       \N       \N       \N        \N         Biography:\N        \N       \N       \N        \N         Tommaso Foscale completed his bachelor studies and Master's on Computer Science at Politecnico di Torino, Turin, in 2019 and 2021. He is on the second year of Ph.D. at Politecnico di Torino. His research interests include wafer testing, machine learning and software programming.\N        \N       \N       \N        \N         \N          Exploiting The Connectivity Metric In Test Programs Generation\N         \N         \N          \N           \N          \N         \N        \N        \N         \N          \N           Author:\N          \N          Lorenzo Cardone\N         \N        \N        \N         \N          \N           Affiliation:\N          \N          Politecnico di Torino (IT)\N         \N        \N        \N        \N         \N          Abstract:\N         \N        \N        \N         \N          The escalating complexity of today’s systems-on-chip (SoCs) poses an increasing challenge to verifying their full functionality. Modern applications of such devices often require stringent safety standards. To uphold such a rigorous level of quality, the need for lengthy procedures that ensure high fault coverage has become increasingly crucial. Multiple techniques to speed up this process have been proposed over the years but, in this article, we will focus on the connectivity metric proposed by Francesco Angione et al. We will discuss the possibility of using the proposed metric to shorten the time required to generate a test program. We will exploit a well-known test program generation tool, known as microGP, evolving an initial population focusing first on the connectivity metric to quickly reach a population of individuals with reasonable features before switching to the traditional fault simulation metric.\N         \N        \N        \N        \N         \N          Biography:\N         \N        \N        \N         \N          Lorenzo Cardone is PhD student at Politecnico di Torino, where he graduated in 2021 in Computer Science. \NHis main research topics are software parallelization and optimization, and he has recently started working in the field of hardware testing.\N         \N        \N        \N         \N          \N           Agile Methodologies applied to IC’s testing\N          \N          \N           \N            \N           \N          \N         \N         \N          \N           \N            Authors:\N           \N           JEY NITHYANANDAM, W ALBAN HAYNSE IMMANUEL, Jay Pankaj SHAH, Khoushikh SAMPATH\N          \N         \N         \N          \N           \N            Affiliation:\N           \N           Infineon Technologies (US)\N          \N         \N         \N         \N          \N           Abstract:\N          \N         \N         \N          \N           Agile processes have important applications in the areas of software project management,\Nsoftware schedule management, etc. In particular the aim of agile processes is to satisfy the customer, faster\Ndevelopment times with lower defects rate. This paper exposes how it’s possible to apply the agile processes to\Nhave a robust IC’s test development environment, elevating applications program and DUT debug.\N          \N         \N         \N         \N          \N           Biography:\N          \N         \N         \N          \N           Christian Pernaci received the B.Sc degree in electronic engineering and the M.sc degree in Automation and Complex Systems Control  engineering  in 2007 and 2010 respectively, from the University of Catania. In 2010 he joined the R&D department of Magneti Marelli Powertrain.\NFrom September 2022 he is senior application engineer for Synergie CAD Instruments s.r.l\N          \N         \N         \N          \N           \N            A Methodology on Validating the Vector DSP Processor in a Heterogeneous Microcontroller Using System Level-Notation\N           \N           \N            \N             \N            \N           \N          \N          \N           \N            \N             Author:\N            \N            Meghashyam Ashwathnarayan\N           \N          \N          \N           \N            \N             Affiliation:\N            \N            Infineon Technologies (IN)\N           \N          \N          \N          \N           \N            Abstract:\N           \N          \N          \N           \N            Infineon’s AURIXTM TC4xx microcontroller family is a significant breakthrough in the automotive industry, especially for safety and security. These microcontrollers are designed to meet the needs of next-gen eMobility, ADAS, automotive E/E architectures, and affordable AI applications. The advanced TriCoreTM 1.8 and scalable AURIXTM accelerator suite powers them, with the latest VDSP unit and various smart accelerators, which enhance performance. This paper provides a comprehensive overview of a post-silicon methodology that uses Cadence’s PerspecTM System Verifier tool to validate the accelerator PPU (Parallel Processing Unit) at a system level. The proposed methodology includes creating a model, defining specific constraints, building an action, and solving compound actions. It also involves creating a regression suite and running it on the SoC level to validate the algorithm run. The approach uses multiple datasets to validate the algorithm, increasing confidence in the results’ accuracy. The post-silicon methodology ensures thorough testing by automatically amplifying the use-case state space, covering more corner cases and high-level application scenarios. By following this methodology, the proposed Perspec tool allows for easy test intent and test suite portability to derivative projects, leading to more accurate, reliable, and efficient automotive MCU processing for safety and security, next-gen eMobility, ADAS, automotive E/E architectures, and affordable AI applications. This paper aims to help industry professionals and researchers conduct system-level validation effectively.\N           \N          \N          \N           \N            \N             Cross-talk aware Small Delay Defect Test with weighted Slack Data\N            \N            \N             \N              \N             \N            \N           \N           \N            \N             \N              Author:\N             \N             Dohan LEE\N            \N           \N           \N            \N             \N              Affiliation:\N             \N             Samsung Electronics Co (KR)\N            \N           \N           \N           \N            \N             Abstract:\N            \N           \N           \N            \N             With the shrinking of semiconductor processes,\Nthere is a growing risk that delay defects escaped at the test stage,\Ninduced by cross-talk delta delay from aggressor signals, can\Ndirectly affect product life cycle. The test methodology for the\Ndefect is becoming increasingly important, and among them,\NDFT(Design for Testability)-related studies on Small Delay\NDefect(SDD) are continuing. In order to generate vectors for\NSDD test, a method of using slack data extracted through Static\NTiming Analysis(STA) is additionally applied to the traditional\NTransition Delay Faults(TDF) Automatic Test Pattern\NGeneration (ATPG) method in general. In this slack-based SDD\NATPG method, the detection rate of SDD compared to the\Ntraditional TDF ATPG can be increased, but since the critical\Npath is recognized from the path-slack perspective regardless of\Nthe cross-talk induced delta delay value of the design node, the\Npriority for defects caused by interference between signals that\Nmay occur in the post silicon cannot be specified. This paper\Nproposes a method in which SDD of a signal vulnerable to\Ninterference signals can be considered more preferentially by\Napplying the weighted delay value when extracting slack data\Nfor each design node from STA. Through the experiments\Napplying the proposed method for the recent 4nm SOC design\Nblocks, it was demonstrated that the number of detectable faults\Nat cross-talk victim SDD is increased at least 10.25% for the\Nworst 15% slack paths compared to the existing SDD ATPG.\N            \N           \N           \N            \N             \N              Automation of PMU module using POP for TTR\N             \N             \N              \N               \N              \N             \N            \N            \N             \N              \N               Authors:\N              \N              Christina Kichenamourty, Jeyendran Nithyanadam, Sonia Kagale\N             \N            \N            \N             \N              \N               Affiliation:\N              \N              Infineon Technologies (IN)\N             \N            \N            \N            \N             \N              Abstract:\N             \N            \N            \N             \N              PMU block is the core of any chip and supplies power to different modules like BT, WLAN, Digital Core, Clock Circuitry, IO interfaces etc. This module tested in SoC combo chips using conventional method consumes 15% - 18% of the overall test time and it directly relates to increased test cost. As a result, emphasis has to be placed on reducing test time, ensure quality and stability of the chip for customer delivery. For every project it is important to follow base line test times and spend minimal effort in product lifecycle bring-up. The POP (Pattern Oriented Programming) method has the feasibility to reduce test time by approximately 50%. However, the effort to bring up is manual and error prone. This paper aims at addressing this issue by automating the POP implementation, thereby reducing bring-up time and TAT (turn-around time) of the product.\N             \N            \N            \N             \N              \N               Optimizing Digital Block Debug on ATE using Flat Pattern to Register Trace conversion\N              \N              \N               \N                \N               \N              \N             \N             \N              \N               \N                Authors:\N               \N               Alban Haynse Immanuel, Jeyendran Nithyanadam, Jay Pankaj Shah, Khoushikh S\N              \N             \N             \N              \N               \N                Affiliation:\N               \N               Infineon Technologies (IN)\N              \N             \N             \N             \N              \N               Abstract:\N              \N             \N             \N              \N               Testing the manufacturing integrity of chips has historically been accomplished using an in-circuit Test machine and a bed-of-nails test fixture. The boundary scan operation of a JTAG device is controlled by a finite state machine known as a TAP (Test Access Port) controller and that of a SWD is controlled by a packet based serial transmission protocol. A register write consists of two parameters, the register address, and the data. If a pattern has failing cycles, it is very hard to debug since it’s an array of binary data. On the other hand, if it’s a register trace it’s easy to figure out the failing bit and check for its functionality. Our studies and experiments highlight the use of the conversion tool on ATE for digital block bring up. The conversion tool consists of two parts, conversion of flat/binary pattern to register trace and vice versa. This helps both the DFT engineers and the Test engineers in debugging the issue.\N              \N             \N             \N              \N               \N                Adaptive Test Time Reduction for IoT devices in ATE\N               \N               \N                \N                 \N                \N               \N              \N              \N               \N                \N                 Authors:\N                \N                Alban Haynse Immanuel, Jeyendran Nithyanadam, Ragotham Hari, Khoushikh S, Naveen S\N               \N              \N              \N               \N                \N                 Affiliation:\N                \N                Infineon Technologies (IN)\N               \N              \N              \N              \N               \N                Abstract:\N               \N              \N              \N               \N                To ensure the cost effectiveness of semiconductor testing, the time taken for unit testing on automated test equipment (ATE) is of utmost importance. Various functional tests are performed on each unit to identify and eliminate defective dies. As tester time incurs significant expenses, the reduction of test time per wafer is crucial for manufacturers in their efforts to lower costs. This paper presents a new test methodology for incorporating Adaptive Test Time Reduction (ATTR) within the test flow by strategically sampling tests which exhibit least probable failures. The expected reduction in test time by implementing the proposed methodology is approximately 13 to 20 %.\N               \N              \N             \N            \N           \N          \N         \N        \N       \N      \N     \N    \N   \N  \N \N\N
X-ALT-DESC;FMTTYPE=text/html:<div class="calendar-authors"> <p>  <b>   Moderator:  </b>  Paolo Rech </p></div><div class="calendar-affiliations"> <p>  <b>   Affiliation:  </b>  Trento University (IT) </p></div><div class="calendar-item"> <div class="header-wrapper">  <h3 class="calendar-paperheader">   On Parametrized Virtual Testing and Simulation of Verilog-AMS Behavioral Models  </h3>  <div class="pdficon filter-blue">   <a href="/index.php/download?filename=PF-1.pdf" target="_blank">    <img src="/files/pdficon.svg"/>   </a>  </div> </div> <div class="calendar-authors">  <p>   <b>    Authors:   </b>   Thorben Schey   <sup>    1   </sup>   , Khaled Karoonlatifi   <sup>    2   </sup>   , Andrey Morozov   <sup>    1   </sup>   , Michael Weyrich   <sup>    1   </sup>  </p> </div> <div class="calendar-affiliations">  <p>   <b>    Affiliations:   </b>   <sup>    1   </sup>   University of Stuttgart Stuttgart (DE),   <sup>    2   </sup>   Advantest Europe GmbH (DE)  </p> </div> <input class="abstract-toggle" id="abstract-toggle-2" type="checkbox"/> <label class="collapsible" for="abstract-toggle-2">  <b>   Abstract:  </b> </label> <div class="calendar-abstract abstract-content">  <p align="justify">   The traditional development of test programs for Analog Mixed Signal (AMS) circuits is both time-consuming and cost-intensive, with validation of only physically available chips. The testing is conducted by providing a set of test inputs. The type of inputs and the optimal sequence is highly dependent on the Circuit Under Test (CUT) and therefore requires expert knowledge. Hence, a desirable solution is a virtual test framework that enables pre-tapeout testing and aids the design phase with early feedback regarding the fulfillment of circuit specifications. In this extended abstract, we introduce a concept of a new simulative approach based on behavioral models featuring parameterizable fault characteristics in Verilog-AMS models. The proposed framework also encompasses models of tester instruments and signal transmission paths. Preliminary simulation results are provided showcasing the feasibility of this approach.  </p> </div> <input class="abstract-toggle" id="biography-toggle-2" type="checkbox"/> <label class="collapsible" for="biography-toggle-2">  <b>   Biography:  </b> </label> <div class="calendar-abstract abstract-content">  <p align="justify">   Thorben Schey received the M.Sc. degree with distinction in electrical engineering and information technology from the University of Stuttgart, Germany, in 2023, where he is currently pursuing the Ph.D. degree at the Institute of Industrial Automation and Software Engineering (IAS). He is also a Doctoral Researcher at IAS. His research interests include virtual testing and behavioral models for chip test.  </p> </div> <div class="calendar-item">  <div class="header-wrapper">   <h3 class="calendar-paperheader">    Techniques for Building Reliable and Energy-Efficient Hardware Accelerators for Dynamic Deep Neural Networks   </h3>   <div class="pdficon filter-blue">    <a href="/index.php/download?filename=PF-2.pdf" target="_blank">     <img src="/files/pdficon.svg"/>    </a>   </div>  </div>  <div class="calendar-authors">   <p>    <b>     Authors:    </b>    Rama Mounika Kodamanchili, Maksim Jenihhin   </p>  </div>  <div class="calendar-affiliations">   <p>    <b>     Affiliation:    </b>    Tallinn University of Technology (EE)   </p>  </div>  <input class="abstract-toggle" id="abstract-toggle-3" type="checkbox"/>  <label class="collapsible" for="abstract-toggle-3">   <b>    Abstract:   </b>  </label>  <div class="calendar-abstract abstract-content">   <p align="justify">    This paper explores the design and integration of Dynamic Deep Neural Networks (D2NN or DynNN) intohardware accelerators to improve reliability while minimizing energy consumption. To achieve optimal performance, we study the decision-making sub-networks, early-exiting techniques, and pruning strategies. Considering the increasing transistor density in AI chips, we address the critical issue of D2NN vulnerability toradiation-induced soft errors and adversarial attacks, proposing architectural modifications and validation techniques to ensure the integrity of these accelerators for safety-critical applications.   </p>  </div>  <input class="abstract-toggle" id="biography-toggle-3" type="checkbox"/>  <label class="collapsible" for="biography-toggle-3">   <b>    Biography:   </b>  </label>  <div class="calendar-abstract abstract-content">   <p align="justify">    I am a first-year Ph.D. student at Tallinn University of Technology, studying under the supervision of Prof. Dr. Maksim Jenihhin. My research focuses on the Reliability of Dynamic Neural Network (D2NN) Hardware accelerators. Additionally, I am a full-time DFX Pre-Silicon Validation Engineer at Intel. I develop System Verilog-based UVM test cases for IP and sub-system validation. Furthermore, I conduct digital fault grading of HVM test cases before delivering them to post-silicon customers.   </p>  </div>  <div class="calendar-item">   <div class="header-wrapper">    <h3 class="calendar-paperheader">     Design of efficient Hardware Inference Engines for Edge AI    </h3>    <div class="pdficon filter-blue">     <a href="/index.php/download?filename=PF-3.pdf" target="_blank">      <img src="/files/pdficon.svg"/>     </a>    </div>   </div>   <div class="calendar-authors">    <p>     <b>      Authors:     </b>     Ahsan Rafiq, Maksim Jenihhin    </p>   </div>   <div class="calendar-affiliations">    <p>     <b>      Affiliation:     </b>     Tallinn University of Technology (EE)    </p>   </div>   <input class="abstract-toggle" id="abstract-toggle-4" type="checkbox"/>   <label class="collapsible" for="abstract-toggle-4">    <b>     Abstract:    </b>   </label>   <div class="calendar-abstract abstract-content">    <p align="justify">     Very recently, the rampant fusion of AI algorithms with computing hardware for edge AI devices has raised theconcerns for designing the delay and power-area efficient, and reliable hardware architectures. The imperative motivation behind this work is to bridge the gap between increasing computational demands and the limitations of AI hardware. This PhD work aims to introduce novel approaches for advanced and optimized hardware architectures to improve the performance parameters of delay, power and area for efficient AI hardware.    </p>   </div>   <input class="abstract-toggle" id="biography-toggle-4" type="checkbox"/>   <label class="collapsible" for="biography-toggle-4">    <b>     Biography:    </b>   </label>   <div class="calendar-abstract abstract-content">    <p align="justify">     Ahsan Rafiq, is an early stage researcher in Tallinn University of Technology since October 2023. He did his master thesis research in high speed and low power digital circuits and systems’ design. Therefore, it was my motivation earlier to work in this field due to my expertise and growing research in this field of digital system design. So, I have decided to pursue my career further in research and academia. This is my motivation for PhD.    </p>   </div>   <div class="calendar-item">    <div class="header-wrapper">     <h3 class="calendar-paperheader">      Deploying Compact and Dependable DNNs in Safety-critical Applications     </h3>     <div class="pdficon filter-blue">      <a href="/index.php/download?filename=PF-4.pdf" target="_blank">       <img src="/files/pdficon.svg"/>      </a>     </div>    </div>    <div class="calendar-authors">     <p>      <b>       Authors:      </b>      Leonardo Alexandrino de Melo      <sup>       1      </sup>      , Alberto Bosio      <sup>       1, 2      </sup>      , Rodrigo Possamai Bastos      <sup>       2      </sup>     </p>    </div>    <div class="calendar-affiliations">     <p>      <b>       Affiliations:      </b>      <sup>       1      </sup>      Ecole Centrale de Lyon (FR),      <sup>       2      </sup>      Univ. Grenoble Alpes,CNRS,Grenoble (FR)     </p>    </div>    <input class="abstract-toggle" id="abstract-toggle-5" type="checkbox"/>    <label class="collapsible" for="abstract-toggle-5">     <b>      Abstract:     </b>    </label>    <div class="calendar-abstract abstract-content">     <p align="justify">      This work aims at developing generation methods for compacting Deep Neural Networks (DNNs) to fit low-power micro controllers used in safety and mission-critical applications. The objective is to implement a Network Architecture Search (NAS) framework to identify the best DNN implementation for a set of application and system requirements, accounting for different embedded computers. To achieve this objective, weintend to develop a bottom up approach in which we characterize DNN components (e.g., convolutional layer, fully connected layer) in terms of energy efficiency and reliability. In this paper, we target the reliability characterization of DNNs through radiation experiments. Thermal neutron beaming will reveal hardware error models that will be used to simulate assorted hardening techniques. After observing and comparing several DNN algo rithms, on different embedded hardware architectures, these combinations will be validated on new radiation tests. It is expected to understand specific weaknesses of each system and which hardening solutions can circumvent radiation effects on each hardware/software combination.     </p>    </div>    <input class="abstract-toggle" id="biography-toggle-5" type="checkbox"/>    <label class="collapsible" for="biography-toggle-5">     <b>      Biography:     </b>    </label>    <div class="calendar-abstract abstract-content">     <p align="justify">      Leonardo Alexandrino is an Aerospace Engineer and Mechatronics Technician from Brazil, who started his PhD this year at Lyon Institute of Nanotechnology at Ecole Centrale de Lyon, in France. His topics of research are: Deep Neural Networks, Embedded Systems, Automation, Nanosatellites, Test and Systems Engineering.     </p>    </div>    <div class="calendar-item">     <div class="header-wrapper">      <h3 class="calendar-paperheader">       Towards Ultra-Reliable Automotive Systems-on-Chip      </h3>      <div class="pdficon filter-blue">       <a href="/index.php/download?filename=PF-5.pdf" target="_blank">        <img src="/files/pdficon.svg"/>       </a>      </div>     </div>     <div class="calendar-authors">      <p>       <b>        Author:       </b>       Giusy Iaria      </p>     </div>     <div class="calendar-affiliations">      <p>       <b>        Affiliation:       </b>       Politecnico di Torino (IT)      </p>     </div>     <input class="abstract-toggle" id="abstract-toggle-6" type="checkbox"/>     <label class="collapsible" for="abstract-toggle-6">      <b>       Abstract:      </b>     </label>     <div class="calendar-abstract abstract-content">      <p align="justify">       Embedded nano-electronic systems are becoming more prevalent in people’s daily lives. As a result, chip andembedded system manufacturing has become increasingly complicated and huge in recent years. As a result, manufacturers are faced with significant complexity in testing their embedded systems to ensure the needed reliability for safety-critical fields. This Ph.D. thesis aims at introducing novel testing approachesto face the complexity of new and more complex industrial case studies.      </p>     </div>     <input class="abstract-toggle" id="biography-toggle-6" type="checkbox"/>     <label class="collapsible" for="biography-toggle-6">      <b>       Biography:      </b>     </label>     <div class="calendar-abstract abstract-content">      <p align="justify">       Giusy Iaria is a Ph.D. student in her third year inComputer and Control Engineering at Polytechnicof Turin, Italy. She is part of the CAD &amp; Reliabilityresearch group with a strong focus on Embeddedelectronics testing. After a research fellowship onthe reliability of safety-critical devices, she started her Ph.D. to find strategies and evaluation methods to reach ultra-reliability in Automotive Systems-on-Chip.      </p>     </div>     <div class="calendar-item">      <div class="header-wrapper">       <h3 class="calendar-paperheader">        System-Level Test techniques for Automotive SoCs       </h3>       <div class="pdficon filter-blue">        <a href="/index.php/download?filename=PF-6.pdf" target="_blank">         <img src="/files/pdficon.svg"/>        </a>       </div>      </div>      <div class="calendar-authors">       <p>        <b>         Author:        </b>        Francesco Angione       </p>      </div>      <div class="calendar-affiliations">       <p>        <b>         Affiliation:        </b>        Politecnico di Torino (IT)       </p>      </div>      <input class="abstract-toggle" id="abstract-toggle-7" type="checkbox"/>      <label class="collapsible" for="abstract-toggle-7">       <b>        Abstract:       </b>      </label>      <div class="calendar-abstract abstract-content">       <p align="justify">        No Abstract       </p>      </div>      <input class="abstract-toggle" id="biography-toggle-7" type="checkbox"/>      <label class="collapsible" for="biography-toggle-7">       <b>        Biography:       </b>      </label>      <div class="calendar-abstract abstract-content">       <p align="justify">        Francesco Angione is a Computer Engineer with an M.Sc., Embedded Systems track, obtained from Politecnico di Torino in 2020. Currently, he is working towards a Ph.D. on "System-Level-Test techniques for Automotive SoCs" at Politecnico di Torino in the CAD &amp; Reliability group. His main interests are real-time operating systems, computer architectures, and their dependability.       </p>      </div>      <div class="calendar-item">       <div class="header-wrapper">        <h3 class="calendar-paperheader">         Leveraging FPGAs for Faster and Less Memory-Demanding Burn-In Testing        </h3>        <div class="pdficon filter-blue">         <a href="/index.php/download?filename=PF-7.pdf" target="_blank">          <img src="/files/pdficon.svg"/>         </a>        </div>       </div>       <div class="calendar-authors">        <p>         <b>          Author:         </b>         Tommaso Foscale        </p>       </div>       <div class="calendar-affiliations">        <p>         <b>          Affiliation:         </b>         Politecnico di Torino (IT)        </p>       </div>       <input class="abstract-toggle" id="abstract-toggle-8" type="checkbox"/>       <label class="collapsible" for="abstract-toggle-8">        <b>         Abstract:        </b>       </label>       <div class="calendar-abstract abstract-content">        <p align="justify">         In recent years, we have seen exponential growth in the complexity of SoCs. More powerful and efficient chips have been developed and integrated into more complex and increasingly interconnected environments. However, this increase in complexity has translated into an increase in the difficulty of testing the correct functioning of these chips. Techniques used in the past have proven insufficient to cope with this increase in complexity. Today, more than ever, it is necessary to find new testing techniques or modify existing techniques to guarantee the correct functioning of the chips once they have passed the testing phase. In this article, we will discuss how, through a board equipped with an FPGA, it is possible to implement a fast burn-in test to ensure the quality of a device, which was carried out using an STMicroelectronics chip from the SPC58 family.        </p>       </div>       <input class="abstract-toggle" id="biography-toggle-8" type="checkbox"/>       <label class="collapsible" for="biography-toggle-8">        <b>         Biography:        </b>       </label>       <div class="calendar-abstract abstract-content">        <p align="justify">         Tommaso Foscale completed his bachelor studies and Master's on Computer Science at Politecnico di Torino, Turin, in 2019 and 2021. He is on the second year of Ph.D. at Politecnico di Torino. His research interests include wafer testing, machine learning and software programming.        </p>       </div>       <div class="calendar-item">        <div class="header-wrapper">         <h3 class="calendar-paperheader">          Exploiting The Connectivity Metric In Test Programs Generation         </h3>         <div class="pdficon filter-blue">          <a href="/index.php/download?filename=PF-8.pdf" target="_blank">           <img src="/files/pdficon.svg"/>          </a>         </div>        </div>        <div class="calendar-authors">         <p>          <b>           Author:          </b>          Lorenzo Cardone         </p>        </div>        <div class="calendar-affiliations">         <p>          <b>           Affiliation:          </b>          Politecnico di Torino (IT)         </p>        </div>        <input class="abstract-toggle" id="abstract-toggle-9" type="checkbox"/>        <label class="collapsible" for="abstract-toggle-9">         <b>          Abstract:         </b>        </label>        <div class="calendar-abstract abstract-content">         <p align="justify">          The escalating complexity of today’s systems-on-chip (SoCs) poses an increasing challenge to verifying their full functionality. Modern applications of such devices often require stringent safety standards. To uphold such a rigorous level of quality, the need for lengthy procedures that ensure high fault coverage has become increasingly crucial. Multiple techniques to speed up this process have been proposed over the years but, in this article, we will focus on the connectivity metric proposed by Francesco Angione et al. We will discuss the possibility of using the proposed metric to shorten the time required to generate a test program. We will exploit a well-known test program generation tool, known as microGP, evolving an initial population focusing first on the connectivity metric to quickly reach a population of individuals with reasonable features before switching to the traditional fault simulation metric.         </p>        </div>        <input class="abstract-toggle" id="biography-toggle-9" type="checkbox"/>        <label class="collapsible" for="biography-toggle-9">         <b>          Biography:         </b>        </label>        <div class="calendar-abstract abstract-content">         <p align="justify">          Lorenzo Cardone is PhD student at Politecnico di Torino, where he graduated in 2021 in Computer Science. His main research topics are software parallelization and optimization, and he has recently started working in the field of hardware testing.         </p>        </div>        <div class="calendar-item">         <div class="header-wrapper">          <h3 class="calendar-paperheader">           Agile Methodologies applied to IC’s testing          </h3>          <div class="pdficon filter-blue">           <a href="/index.php/download?filename=PF-9.pdf" target="_blank">            <img src="/files/pdficon.svg"/>           </a>          </div>         </div>         <div class="calendar-authors">          <p>           <b>            Authors:           </b>           JEY NITHYANANDAM, W ALBAN HAYNSE IMMANUEL, Jay Pankaj SHAH, Khoushikh SAMPATH          </p>         </div>         <div class="calendar-affiliations">          <p>           <b>            Affiliation:           </b>           Infineon Technologies (US)          </p>         </div>         <input class="abstract-toggle" id="abstract-toggle-11" type="checkbox"/>         <label class="collapsible" for="abstract-toggle-11">          <b>           Abstract:          </b>         </label>         <div class="calendar-abstract abstract-content">          <p align="justify">           Agile processes have important applications in the areas of software project management,software schedule management, etc. In particular the aim of agile processes is to satisfy the customer, fasterdevelopment times with lower defects rate. This paper exposes how it’s possible to apply the agile processes tohave a robust IC’s test development environment, elevating applications program and DUT debug.          </p>         </div>         <input class="abstract-toggle" id="biography-toggle-11" type="checkbox"/>         <label class="collapsible" for="biography-toggle-11">          <b>           Biography:          </b>         </label>         <div class="calendar-abstract abstract-content">          <p align="justify">           Christian Pernaci received the B.Sc degree in electronic engineering and the M.sc degree in Automation and Complex Systems Control  engineering  in 2007 and 2010 respectively, from the University of Catania. In 2010 he joined the R&amp;D department of Magneti Marelli Powertrain.From September 2022 he is senior application engineer for Synergie CAD Instruments s.r.l          </p>         </div>         <div class="calendar-item">          <div class="header-wrapper">           <h3 class="calendar-paperheader">            A Methodology on Validating the Vector DSP Processor in a Heterogeneous Microcontroller Using System Level-Notation           </h3>           <div class="pdficon filter-blue">            <a href="/index.php/download?filename=PF-10.pdf" target="_blank">             <img src="/files/pdficon.svg"/>            </a>           </div>          </div>          <div class="calendar-authors">           <p>            <b>             Author:            </b>            Meghashyam Ashwathnarayan           </p>          </div>          <div class="calendar-affiliations">           <p>            <b>             Affiliation:            </b>            Infineon Technologies (IN)           </p>          </div>          <input class="abstract-toggle" id="abstract-toggle-12" type="checkbox"/>          <label class="collapsible" for="abstract-toggle-12">           <b>            Abstract:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            Infineon’s AURIXTM TC4xx microcontroller family is a significant breakthrough in the automotive industry, especially for safety and security. These microcontrollers are designed to meet the needs of next-gen eMobility, ADAS, automotive E/E architectures, and affordable AI applications. The advanced TriCoreTM 1.8 and scalable AURIXTM accelerator suite powers them, with the latest VDSP unit and various smart accelerators, which enhance performance. This paper provides a comprehensive overview of a post-silicon methodology that uses Cadence’s PerspecTM System Verifier tool to validate the accelerator PPU (Parallel Processing Unit) at a system level. The proposed methodology includes creating a model, defining specific constraints, building an action, and solving compound actions. It also involves creating a regression suite and running it on the SoC level to validate the algorithm run. The approach uses multiple datasets to validate the algorithm, increasing confidence in the results’ accuracy. The post-silicon methodology ensures thorough testing by automatically amplifying the use-case state space, covering more corner cases and high-level application scenarios. By following this methodology, the proposed Perspec tool allows for easy test intent and test suite portability to derivative projects, leading to more accurate, reliable, and efficient automotive MCU processing for safety and security, next-gen eMobility, ADAS, automotive E/E architectures, and affordable AI applications. This paper aims to help industry professionals and researchers conduct system-level validation effectively.           </p>          </div>          <div class="calendar-item">           <div class="header-wrapper">            <h3 class="calendar-paperheader">             Cross-talk aware Small Delay Defect Test with weighted Slack Data            </h3>            <div class="pdficon filter-blue">             <a href="/index.php/download?filename=PF-11.pdf" target="_blank">              <img src="/files/pdficon.svg"/>             </a>            </div>           </div>           <div class="calendar-authors">            <p>             <b>              Author:             </b>             Dohan LEE            </p>           </div>           <div class="calendar-affiliations">            <p>             <b>              Affiliation:             </b>             Samsung Electronics Co (KR)            </p>           </div>           <input class="abstract-toggle" id="abstract-toggle-13" type="checkbox"/>           <label class="collapsible" for="abstract-toggle-13">            <b>             Abstract:            </b>           </label>           <div class="calendar-abstract abstract-content">            <p align="justify">             With the shrinking of semiconductor processes,there is a growing risk that delay defects escaped at the test stage,induced by cross-talk delta delay from aggressor signals, candirectly affect product life cycle. The test methodology for thedefect is becoming increasingly important, and among them,DFT(Design for Testability)-related studies on Small DelayDefect(SDD) are continuing. In order to generate vectors forSDD test, a method of using slack data extracted through StaticTiming Analysis(STA) is additionally applied to the traditionalTransition Delay Faults(TDF) Automatic Test PatternGeneration (ATPG) method in general. In this slack-based SDDATPG method, the detection rate of SDD compared to thetraditional TDF ATPG can be increased, but since the criticalpath is recognized from the path-slack perspective regardless ofthe cross-talk induced delta delay value of the design node, thepriority for defects caused by interference between signals thatmay occur in the post silicon cannot be specified. This paperproposes a method in which SDD of a signal vulnerable tointerference signals can be considered more preferentially byapplying the weighted delay value when extracting slack datafor each design node from STA. Through the experimentsapplying the proposed method for the recent 4nm SOC designblocks, it was demonstrated that the number of detectable faultsat cross-talk victim SDD is increased at least 10.25% for theworst 15% slack paths compared to the existing SDD ATPG.            </p>           </div>           <div class="calendar-item">            <div class="header-wrapper">             <h3 class="calendar-paperheader">              Automation of PMU module using POP for TTR             </h3>             <div class="pdficon filter-blue">              <a href="/index.php/download?filename=PF-12.pdf" target="_blank">               <img src="/files/pdficon.svg"/>              </a>             </div>            </div>            <div class="calendar-authors">             <p>              <b>               Authors:              </b>              Christina Kichenamourty, Jeyendran Nithyanadam, Sonia Kagale             </p>            </div>            <div class="calendar-affiliations">             <p>              <b>               Affiliation:              </b>              Infineon Technologies (IN)             </p>            </div>            <input class="abstract-toggle" id="abstract-toggle-14" type="checkbox"/>            <label class="collapsible" for="abstract-toggle-14">             <b>              Abstract:             </b>            </label>            <div class="calendar-abstract abstract-content">             <p align="justify">              PMU block is the core of any chip and supplies power to different modules like BT, WLAN, Digital Core, Clock Circuitry, IO interfaces etc. This module tested in SoC combo chips using conventional method consumes 15% - 18% of the overall test time and it directly relates to increased test cost. As a result, emphasis has to be placed on reducing test time, ensure quality and stability of the chip for customer delivery. For every project it is important to follow base line test times and spend minimal effort in product lifecycle bring-up. The POP (Pattern Oriented Programming) method has the feasibility to reduce test time by approximately 50%. However, the effort to bring up is manual and error prone. This paper aims at addressing this issue by automating the POP implementation, thereby reducing bring-up time and TAT (turn-around time) of the product.             </p>            </div>            <div class="calendar-item">             <div class="header-wrapper">              <h3 class="calendar-paperheader">               Optimizing Digital Block Debug on ATE using Flat Pattern to Register Trace conversion              </h3>              <div class="pdficon filter-blue">               <a href="/index.php/download?filename=PF-13.pdf" target="_blank">                <img src="/files/pdficon.svg"/>               </a>              </div>             </div>             <div class="calendar-authors">              <p>               <b>                Authors:               </b>               Alban Haynse Immanuel, Jeyendran Nithyanadam, Jay Pankaj Shah, Khoushikh S              </p>             </div>             <div class="calendar-affiliations">              <p>               <b>                Affiliation:               </b>               Infineon Technologies (IN)              </p>             </div>             <input class="abstract-toggle" id="abstract-toggle-15" type="checkbox"/>             <label class="collapsible" for="abstract-toggle-15">              <b>               Abstract:              </b>             </label>             <div class="calendar-abstract abstract-content">              <p align="justify">               Testing the manufacturing integrity of chips has historically been accomplished using an in-circuit Test machine and a bed-of-nails test fixture. The boundary scan operation of a JTAG device is controlled by a finite state machine known as a TAP (Test Access Port) controller and that of a SWD is controlled by a packet based serial transmission protocol. A register write consists of two parameters, the register address, and the data. If a pattern has failing cycles, it is very hard to debug since it’s an array of binary data. On the other hand, if it’s a register trace it’s easy to figure out the failing bit and check for its functionality. Our studies and experiments highlight the use of the conversion tool on ATE for digital block bring up. The conversion tool consists of two parts, conversion of flat/binary pattern to register trace and vice versa. This helps both the DFT engineers and the Test engineers in debugging the issue.              </p>             </div>             <div class="calendar-item">              <div class="header-wrapper">               <h3 class="calendar-paperheader">                Adaptive Test Time Reduction for IoT devices in ATE               </h3>               <div class="pdficon filter-blue">                <a href="/index.php/download?filename=PF-14.pdf" target="_blank">                 <img src="/files/pdficon.svg"/>                </a>               </div>              </div>              <div class="calendar-authors">               <p>                <b>                 Authors:                </b>                Alban Haynse Immanuel, Jeyendran Nithyanadam, Ragotham Hari, Khoushikh S, Naveen S               </p>              </div>              <div class="calendar-affiliations">               <p>                <b>                 Affiliation:                </b>                Infineon Technologies (IN)               </p>              </div>              <input class="abstract-toggle" id="abstract-toggle-16" type="checkbox"/>              <label class="collapsible" for="abstract-toggle-16">               <b>                Abstract:               </b>              </label>              <div class="calendar-abstract abstract-content">               <p align="justify">                To ensure the cost effectiveness of semiconductor testing, the time taken for unit testing on automated test equipment (ATE) is of utmost importance. Various functional tests are performed on each unit to identify and eliminate defective dies. As tester time incurs significant expenses, the reduction of test time per wafer is crucial for manufacturers in their efforts to lower costs. This paper presents a new test methodology for incorporating Adaptive Test Time Reduction (ATTR) within the test flow by strategically sampling tests which exhibit least probable failures. The expected reduction in test time by implementing the proposed methodology is approximately 13 to 20 %.               </p>              </div>             </div>            </div>           </div>          </div>         </div>        </div>       </div>      </div>     </div>    </div>   </div>  </div> </div></div>
LAST-MODIFIED:20240417T112019Z
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LOCATION:Johan de Wittlaan 30\, 2517 JR Den Haag\, Zuid-Holland\, Netherlands
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DTSTART;TZID=Europe/Amsterdam:20240522T101500
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SUMMARY:Coffee Break - PhD Forum 2 and McCluskey Posters
CREATED:20240313T102043Z
DTSTAMP:20240313T102043Z
URL:https://ets24.ewi.tudelft.nl/index.php/home/program/conference-program/coffee-break-phd-forum-2-and-mccluskey-posters
DESCRIPTION:\N \N  \N   Moderator:\N  \N  Angeliki Kritikakou\N \N\N\N \N  \N   Affiliation:\N  \N  University of Rennes (FR)\N \N\N\N \N  \N   Enhancing Assertion-Based Verification in Hardware Designs through Data Mining\N  \N  \N   \N    \N   \N  \N \N \N  \N   \N    Authors:\N   \N   Mohammad Reza Heidari Iman, Tara Ghasempouri\N  \N \N \N  \N   \N    Affiliation:\N   \N   Tallinn University of Technology (EE)\N  \N \N \N \N  \N   Abstract:\N  \N \N \N  \N   In the realm of assertion-based verification, several assertion miners automatically generate assertions for verifying hardware designs. Despite their promising features, these min- ers suffer from drawbacks such as high execution time and low mutant detection coverage. In this research, we introduce a data mining-based assertion miner to automatically mine assertions. Our experiments show that our assertion miner generates fewer but more accurate assertions compared to other miners.\N  \N \N \N \N  \N   Biography:\N  \N \N \N  \N   Mohammad Reza Heidari Iman is a Ph.D. candidate in the Department of Computer Systems at Tallinn University of Technology, Estonia. His research interests include verification, assertion-based verification, and security verification in embedded systems. He also explores the application of data mining in hardware verification.\N  \N \N \N  \N   \N    Manufacturing and In-Field Testing Techniques\N   \N   \N    \N     \N    \N   \N  \N  \N   \N    \N     Author:\N    \N    Gabriele Filipponi\N   \N  \N  \N   \N    \N     Affiliation:\N    \N    Politecnico di Torino (IT)\N   \N  \N  \N  \N   \N    Abstract:\N   \N  \N  \N   \N    The automotive industry is currently undergoing a period of rapid technological advancement. This is evidenced by the significant shift towards fully electric vehicles equipped with complex integrated circuits. This surge in technological complexity necessitates the implementation of more rigorous and adaptable testing methodologies throughout entire device lifecycle, encompassing all stages from initial manufacturing to final disposal. While traditional testing approaches still hold value, it is crucial to explore and develop novel techniques to effectively navigate this rapidly evolving landscape and ensure the safety and functionality of these increasingly sophisticated devices.\N   \N  \N  \N  \N   \N    Biography:\N   \N  \N  \N   \N    G. Filipponi is a second-year Ph.D student in Computer and Control Engineering at Politecnico di Torino. He is part of the Electronic CAD & Reliability group, with a focus on Embedded electronics testing. His main field of research regards ultra reliability for in-field Automotive SoC, specifically exploiting the Logic BIST.\N   \N  \N  \N   \N    \N     Time Guarantee and Reliable Execution for Safety-Critical Real-Time Systems\N    \N    \N     \N      \N     \N    \N   \N   \N    \N     \N      Authors:\N     \N     Pegdwende Romaric Nikiema, Angeliki Kritikakou, Marcello Traiola, Olivier Sentieys\N    \N   \N   \N    \N     \N      Affiliation:\N     \N     University of Rennes (FR)\N    \N   \N   \N   \N    \N     Abstract:\N    \N   \N   \N    \N     Safety-critical real-time embedded systems must guarantee both reliable and in-time execution. The increasing complexity of embedded systems and the reduction in technology size make modern processors more vulnerable to faults, especially in multicore systems, where it’s difficult to predict the timing behavior due to numerous interference. Faults impact not only the functional correctness of the application but also the timing correctness, which is paramount for safety-critical systems. The goal is to propose means to evaluate systems in the presence of faults and propose techniques that mitigate the errors to allow a safe execution. For the first step discussed in the following, we enhance vulnerability analysis to include functional and timing correctness, show that faults impact Worst-Case Execution Time (WCET) estimations, and propose a fault tolerance technique.\N    \N   \N   \N   \N    \N     Biography:\N    \N   \N   \N    \N     Pegdwende Romaric NIKIEMA is a 2nd year Ph.D student at Université de Rennes, France. He received his B.S. degree in Electronic Electrotechnic and Control System from the Université Aube Nouvelle, Burkina Faso in 2020, his M.S. degree in Wireless Embedded Technology from the Nantes Université, France in 2022. His research interests include computer architectures, Real-time and dependable embedded systems.\N    \N   \N   \N    \N     \N      Irradiation Tests: Deriving Memory Design Parameters\N     \N     \N      \N       \N      \N     \N    \N    \N     \N      \N       Authors:\N      \N      N. Kolahimahmoudi, P. Bernardi\N     \N    \N    \N     \N      \N       Affiliation:\N      \N      Politecnico di Torino (IT)\N     \N    \N    \N    \N     \N      Abstract:\N     \N    \N    \N     \N      This paper introduces a new method to derive architectural details from embedded System-on-Chip(SoC) memories. This method can extract memory design configurations (MDCs) such as mirroring, and scrambling utilizing Multiple Cell Upsets (MCUs) generated through a single irradiation test. Discovering the correct MDC corroborates the proposed method although, this method may find redundant MDCs alongside the correct MDC. The number of these redundant MDCs decreased with the increment of the MCUs. This number decreased to an average of 2 possible MDCs when considering 100 MCUs.\N     \N    \N    \N    \N     \N      Biography:\N     \N    \N    \N     \N      Nima Kolahimahmoudi received his master’s in Electronic Engineering at the Polytechnic of Turin in 2022. Currently, he is a Ph.D. student in the Department of Control and Computer Engineering in the CAD and Reliability group under the supervision of Professor Paolo Bernardi. Mainly, his research targets the test and reliability of Automotive Systems-on-Chips, including analog and mixed-signal circuits and embedded memories.\N     \N    \N    \N     \N      \N       A Novel Machine Learning-based Fault Shape Classification for Memories Embedded In Automotive Systems-on-Chip\N      \N      \N       \N        \N       \N      \N     \N     \N      \N       \N        Authors:\N       \N       P. Bernardi, G. Insinga\N      \N     \N     \N      \N       \N        Affiliation:\N       \N       Politecnico di Torino (IT)\N      \N     \N     \N     \N      \N       Abstract:\N      \N     \N     \N      \N       A significant percentage of modern Automotive System-on-Chip dies is occupied by embedded memories. Embedded memories have thus a high impact on the yield of these devices, and their testing and reliable operations are then key priorities for the manufacturers. Embedded memories are tested with a complete suite of tests that verify the correct behavior of the bit cells in various conditions such as supply voltage, operation frequency, temperature, etc. All these tests generate huge quantities of data that are difficult to analyze and to elaborate in an easily understandable form. A useful approach is to divide the failures based on their fault shapes. For example, a series of adjacent bitlines is probably related to a sense amplifier failure, or a series of adjacent wordlines is probably related to a failing minisector. Heuristic programs to recognize these shapes exist, but they are slow, not flexible, and prone to mistakes. In this work, we propose a new shape analysis solution that uses machine learning to recognize fault shapes faster and more reliably. To reach our goals, we trained a convolutional neural network (CNN) called ResNet18. This net is very famous in the literature for having high performance and being relatively lightweight. It takes its name from the 18 layers that compose it and accepts image tiles of 224x224 pixels and outputs the label of the recognized fault shape. The complete flow includes a preprocessing phase that prepares the input for the neural network and a post-processing phase that combines the output of the neural network to recognize bigger shapes. Experimental results on an Automotive-grade Infineon SoC show the validity of the approach, which correctly recognizes 97.9% of the failure on a set of 8000 failing segments.\N      \N     \N     \N     \N      \N       Biography:\N      \N     \N     \N      \N       Giorgio Insinga received his master's degree in Electronics Engineering at Politecnico di Torino in 2021. He is a Ph.D. student at Politecnico di Torino under the supervision of professor Paolo Bernardi. His primary research focuses on the test and reliability of Automotive Systems-on-Chip and their embedded memories.\N      \N     \N     \N      \N       \N        Exploring Side Channel Attacks on Cutting-Edge Adder-Free SRAM CIM\N       \N       \N        \N         \N        \N       \N      \N      \N       \N        \N         Authors:\N        \N        Fouwad Jamil Mir, Abdullah Aljuffri, Mottaqiallah Taouil\N       \N      \N      \N       \N        \N         Affiliation:\N        \N        Delft University of Technology (NL)\N       \N      \N      \N      \N       \N        Abstract:\N       \N      \N      \N       \N        A novel adder-free SRAM-based Computation-in-Memory (CIM) accelerator for Binary Neural Networks (BNNs) is susceptible to power side-channel attacks. This work proposes a framework to exploit the CIM periphery’s power signature to reverse-engineer SRAM cell weights. The methodology isolates the counter’s power signature, segments the power trace, and profiles these segments to identify weight values. Initial findings based on power profile analysis demonstrate the feasibility of weight information extraction. This work highlights the importance of security in CIM design and motivates research on countermeasures like masking or obfuscation to mitigate power side-channel vulnerabilities. Our contribution is a novel attack approach for digital CIM devices, paving the way for future research in securing these systems.\N       \N      \N      \N      \N       \N        Biography:\N       \N      \N      \N       \N        Fouwad Mir is a Ph.D. candidate in Computer Engineering at the faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Delft University of Technology (TU-Delft). His research focuses on hardware security for novel computer architectures and neural network accelerators. He is enthusiastic about exploring hardware security vulnerabilities in digital and mixed-signal designs, aiming to develop mitigation strategies for robust and secure hardware.\N       \N      \N      \N       \N        \N         Pre-Silicon Fuzzing of RISC-V Hardware Components and their Interactions\N        \N        \N         \N          \N         \N        \N       \N       \N        \N         \N          Authors:\N         \N         Gijs Burghoorn, Abdullah Aljuffri, Mottaqiallah Taouil\N        \N       \N       \N        \N         \N          Affiliation:\N         \N         Delft University of Technology (NL)\N        \N       \N       \N       \N        \N         Abstract:\N        \N       \N       \N        \N         In recent years, processor microarchitectures have shown both functional and security issues. Current mitigation-focused research centers around undirected testing of processors, i.e. hardware or processor fuzz testing. More recent promising studies focus on using test programs as fuzz inputs. Cascade, which is the state-of-the-art in these test program focused tools, uses a loosely integrated collection of existing tools. This severely limits what intricate behaviors and chip-component interactions it can test and the speed at which it can generate test programs. We propose a set of tightly integrated tools that allow for the real-time program generation. These tools allow for automation in the behavioral verification and security analysis of intricate interactions between chip-components.\N        \N       \N       \N       \N        \N         Biography:\N        \N       \N       \N        \N         Gijs Burghoorn finished a BSc. Computer Science at Leiden University in the Netherlands. Afterwards, he went to Grenoble in France and got a MSc. in Informatics and Cybersecurity. Here, he performed research into Hardware Security, Side-Channel Analysis and Fault Injection. Afterwards, he started his PhD at the TU Delft in the Netherlands.\N        \N       \N       \N        \N         \N          Online Detection of Unique Faults in RRAMs\N         \N         \N          \N           \N          \N         \N        \N        \N         \N          \N           Authors:\N          \N          Hanzhi Xun\N          \N           1\N          \N          , Moritz Fieback\N          \N           1\N          \N          , Mohammad Amin Yaldagard\N          \N           1\N          \N          , Sicong Yuan\N          \N           1\N          \N          , Hassen Aziza\N          \N           2\N          \N          , Mottaqiallah Taouil\N          \N           1, 3\N          \N          , Said Hamdioui\N          \N           1, 3\N          \N         \N        \N        \N         \N          \N           Affiliations:\N          \N          \N           1\N          \N          Delft University of Technology (NL),\N          \N           2\N          \N          Aix-Marseille University (FR),\N          \N           3\N          \N          CognitiveIC (NL)\N         \N        \N        \N        \N         \N          Abstract:\N         \N        \N        \N         \N          Due to the immature manufacturing process, Resistive Random Access Memories (RRAMs) are prone to exhibit\Nnew failure mechanisms and faults, which should be efficiently detected for high-volume production. Those unique faults are hard to detect but require specific Design-for-Test (DfT) circuit design. This paper proposes a DfT based on a parallel-reference write circuit that can detect all RRAM array faults during diagnosis, production testing, and its application in the field.\N         \N        \N        \N        \N         \N          Biography:\N         \N        \N        \N         \N          Hanzhi Xun received the B.Sc. and M.Eng. degrees from Xidian University, Xi'an, China, in 2018 and 2021, respectively. He received another M.Eng. degree form Waseda University, Kitakyushu, Japan, in 2021. He is currently working toward the Ph.D. degree at the Computer Engineering Laboratory, Delft University of Technology, Delft, The Netherlands. His research interests focus on device modeling, test, and reliability of Resistive RAMs.\N         \N        \N        \N         \N          \N           Reliability Assessment and Optimization of Dynamic DNNs for Edge Accelerators\N          \N          \N           \N            \N           \N          \N         \N         \N          \N           \N            Authors:\N           \N           Georgios Konstantinidis, Maria K. Michail, Theocharis Theocharides\N          \N         \N         \N          \N           \N            Affiliation:\N           \N           University of Cyprus (CY)\N          \N         \N         \N         \N          \N           Abstract:\N          \N         \N         \N          \N           The urge to deploy Machine Learning (ML) algo- rithms for inference on edge devices, has created the need for efficient, resource-constrained and dependable hardware, that can be utilized according to the constraints of the underlying hardware as well as those imposed by the running applications. Recently, various types of dynamic deep neural networks have been introduced, mainly for the purpose of optimizing inference performance for real-time response systems. Such edge-based systems are often resource-constrained and are deployed in various safety-critical and/or harsh environments. Therefore, these systems have increased needs in reliability, while at the same time struggle to satisfy performance and energy requirements. This thesis will first investigate the vulnerability assessment of various dynamic deep neural networks, in order to better understand the impact of these performance optimizations on the overall reliability of the networks. Consequently, the thesis will propose various approaches to co-optimize the network design based on both performance and reliability targets. The proposed approaches will be evaluated within a newly developed simulation framework which allows for efficient yet accurate vulnerability analysis, as it considers hardware-aware software fault models.\N          \N         \N         \N         \N          \N           Biography:\N          \N         \N         \N          \N           Georgios Konstantinidis received his BSc degree in Electrical and Electronic Engineering from the Department of Electrical and Computer Engineering at the University of Cyprus and his MSc degree in Mechatronics from the department of Mechanical Engineering at the University of Leeds. He is currently a PhD student. His research interests include Machine Learning, Embedded Systems, Forecasting Algorithms and ML Accelerators.\N          \N         \N         \N          \N           \N            Dependable Reconfigurable Scan Networks\N           \N           \N            \N             \N            \N           \N          \N          \N           \N            \N             Author:\N            \N            Natalia Lylina\N           \N          \N          \N           \N            \N             Affiliation:\N            \N            University of Stuttgart (DE)\N           \N          \N          \N          \N           \N            Abstract:\N           \N          \N          \N           \N            Today, dependable modern devices are equipped with an increasing number of extra-functional instrument to facilitate cost-efficient bring-up, debug, test, diagnosis, and adaptivity in the field and might include sensors, aging monitors and Built-In Self-Test (BIST) registers e.g. Reconfigurable Scan Networks (RSNs) provide a flexible way to access such instruments as well the device’s registers throughout the lifetime, starting from post-silicon validation (PSV) through manufacturing test and finally during in-field operation. At the same time, the dependability properties of the system can be affected through an improper RSN integration. This doctoral thesis overcomes these problems and establishes a unified method for design automation of dependable RSNs. The developed method considers the most relevant dependability aspects as robustness, testability, and security compliance.\N           \N          \N          \N          \N           \N            Biography:\N           \N          \N          \N           \N            Natalia Lylina received an M.Sc. double degree in computer science from Moscow Power Engineering Institute, Russia, and Technical University of Ilmenau, Germany. Since 2017, she has been with the Institute of\NComputer Architecture and Computer Engineering at the University of Stuttgart as a PhD student, and received her doctoral title (Dr. rer. nat.) in 2022. She is a Member of IEEE. Her research interests include dependable systems, test and diagnosis, and reconfigurable scan networks.\N           \N          \N          \N           \N            \N             Toward Fault-Tolerant Applications on Reconfigurable Systems-on-Chip\N            \N            \N             \N              \N             \N            \N           \N           \N            \N             \N              Authors:\N             \N             Corrado De Sio, Luca Sterpone\N            \N           \N           \N            \N             \N              Affiliation:\N             \N             Politecnico di Torino (IT)\N            \N           \N           \N           \N            \N             Abstract:\N            \N           \N           \N            \N             FPGA-based System-on-Chips (SoCs) have facilitated the integration of software programmability and\Ncustom hardware acceleration. The research work focuses on accurate and efficient robustness analysis for Reconfigurable SoCs, considering their heterogeneous components and radiation induced effects. Research work includes the characterization of components, such as memories, and the comparison of different\Ntechnology processes through radiation testing experiments. Novel methodologies for reliability analysis of reconfigurable systems are proposed based on programmable hardware, supporting automation, integration with the development flow, and improving understanding of the fault affecting the system. The analysis of different components, such as soft and hard processors, host-device interfacing systems, and custom hardware accelerators, is presented, considering different fault models and dedicated evaluation approaches. Research work ranges from the hardware systems to the software stack of reconfigurable SoCs, providing a comprehensive investigation of their heterogeneity and the advantages that this can provide.\N            \N           \N           \N           \N            \N             Biography:\N            \N           \N           \N            \N             CORRADO DE SIO received the M.S. degree in Computer Engineering from the University of Pisa. He received his Ph.D. from Politecnico di Torino in 2023. Currently, He works in the CAD & Reliability group of Politecnico di Torino as a PostDoC . His research interests include reconfigurable devices, radiation effects, and EDA tools for analyzing and improving the reliability and the design of embedded and reconfigurable systems applications.\N            \N           \N           \N            \N             \N              SDfT: Secure Design for Testability\N             \N             \N              \N               \N              \N             \N            \N            \N             \N              \N               Authors:\N              \N              Yogendra Sao, Sk. Subidh Ali\N             \N            \N            \N             \N              \N               Affiliation:\N              \N              Indian Institute of Technology Bhilai (IN)\N             \N            \N            \N            \N             \N              Abstract:\N             \N            \N            \N             \N              Scan-based design for Testability is the de-facto standard for chip testing, which provides high observability and test coverage by enabling direct access to chip memory elements. The scan-based Design-for-Testability (DfT) technique has also become the prime target of attackers whose aim is to extract the secret information embedded inside a chip by misusing its scan infrastructure. This thesis work performs a detailed security analysis (hardware vulnerability analysis and penetration testing) of existing defense mechanisms, discovers new vulnerabilities, and proposes a new defense mechanism against scan attacks.\N             \N            \N            \N            \N             \N              Biography:\N             \N            \N            \N             \N              Yogendra Sao received an M.Tech. in computer science and engineering from the International Institute of Information Technology Hyderabad, India, in 2011 and a Ph.D. in computer science and engineering from the Indian Institute of Technology Bhilai in 2023.\N\NHe is an Assistant Engineer with Chhattisgarh State Power Distribution Company Limited, Raipur, since 2011. His research interests include hardware security and secure design for testability. He has published 9 conference and journal papers.\N             \N            \N            \N             \N              \N               Design for Advanced Optical Test for Image and Photonic Sensors\N              \N              \N               \N                \N               \N              \N             \N             \N              \N               \N                Authors:\N               \N               J. Lefevre\N               \N                1, 2\N               \N               , P. Debaud\N               \N                1\N               \N               , P. Girard\N               \N                2\N               \N               , A. Virazel\N               \N                2\N               \N              \N             \N             \N              \N               \N                Affiliations:\N               \N               \N                1\N               \N               STMicroelectronics (FR),\N               \N                2\N               \N               LIRMM University of Montpellier / CNRS (FR)\N              \N             \N             \N             \N              \N               Abstract:\N              \N             \N             \N              \N               No Abstract\N              \N             \N             \N             \N              \N               Biography:\N              \N             \N             \N              \N               Julia Lefevre completed her PhD at STMicroelectronics Grenoble and the LIRMM (Laboratory of Informatic, Robotic and Microelectronics of Montpellier) in France. She graduated a master degree in microelectronic at the University of Montpellier in 2020 before to work on CMOS image sensor test, looking for innovative test techniques during her PhD. She is now working as a product engineer in the Imaging Division at STMicroelectronics and will defend her PhD in June.\N              \N             \N            \N           \N          \N         \N        \N       \N      \N     \N    \N   \N  \N \N\N
X-ALT-DESC;FMTTYPE=text/html:<div class="calendar-authors"> <p>  <b>   Moderator:  </b>  Angeliki Kritikakou </p></div><div class="calendar-affiliations"> <p>  <b>   Affiliation:  </b>  University of Rennes (FR) </p></div><div class="calendar-item"> <div class="header-wrapper">  <h3 class="calendar-paperheader">   Enhancing Assertion-Based Verification in Hardware Designs through Data Mining  </h3>  <div class="pdficon filter-blue">   <a href="/index.php/download?filename=PS2-1.pdf" target="_blank">    <img src="/files/pdficon.svg"/>   </a>  </div> </div> <div class="calendar-authors">  <p>   <b>    Authors:   </b>   Mohammad Reza Heidari Iman, Tara Ghasempouri  </p> </div> <div class="calendar-affiliations">  <p>   <b>    Affiliation:   </b>   Tallinn University of Technology (EE)  </p> </div> <input class="abstract-toggle" id="abstract-toggle-2" type="checkbox"/> <label class="collapsible" for="abstract-toggle-2">  <b>   Abstract:  </b> </label> <div class="calendar-abstract abstract-content">  <p align="justify">   In the realm of assertion-based verification, several assertion miners automatically generate assertions for verifying hardware designs. Despite their promising features, these min- ers suffer from drawbacks such as high execution time and low mutant detection coverage. In this research, we introduce a data mining-based assertion miner to automatically mine assertions. Our experiments show that our assertion miner generates fewer but more accurate assertions compared to other miners.  </p> </div> <input class="abstract-toggle" id="biography-toggle-2" type="checkbox"/> <label class="collapsible" for="biography-toggle-2">  <b>   Biography:  </b> </label> <div class="calendar-abstract abstract-content">  <p align="justify">   Mohammad Reza Heidari Iman is a Ph.D. candidate in the Department of Computer Systems at Tallinn University of Technology, Estonia. His research interests include verification, assertion-based verification, and security verification in embedded systems. He also explores the application of data mining in hardware verification.  </p> </div> <div class="calendar-item">  <div class="header-wrapper">   <h3 class="calendar-paperheader">    Manufacturing and In-Field Testing Techniques   </h3>   <div class="pdficon filter-blue">    <a href="/index.php/download?filename=PS2-2.pdf" target="_blank">     <img src="/files/pdficon.svg"/>    </a>   </div>  </div>  <div class="calendar-authors">   <p>    <b>     Author:    </b>    Gabriele Filipponi   </p>  </div>  <div class="calendar-affiliations">   <p>    <b>     Affiliation:    </b>    Politecnico di Torino (IT)   </p>  </div>  <input class="abstract-toggle" id="abstract-toggle-3" type="checkbox"/>  <label class="collapsible" for="abstract-toggle-3">   <b>    Abstract:   </b>  </label>  <div class="calendar-abstract abstract-content">   <p align="justify">    The automotive industry is currently undergoing a period of rapid technological advancement. This is evidenced by the significant shift towards fully electric vehicles equipped with complex integrated circuits. This surge in technological complexity necessitates the implementation of more rigorous and adaptable testing methodologies throughout entire device lifecycle, encompassing all stages from initial manufacturing to final disposal. While traditional testing approaches still hold value, it is crucial to explore and develop novel techniques to effectively navigate this rapidly evolving landscape and ensure the safety and functionality of these increasingly sophisticated devices.   </p>  </div>  <input class="abstract-toggle" id="biography-toggle-3" type="checkbox"/>  <label class="collapsible" for="biography-toggle-3">   <b>    Biography:   </b>  </label>  <div class="calendar-abstract abstract-content">   <p align="justify">    G. Filipponi is a second-year Ph.D student in Computer and Control Engineering at Politecnico di Torino. He is part of the Electronic CAD &amp; Reliability group, with a focus on Embedded electronics testing. His main field of research regards ultra reliability for in-field Automotive SoC, specifically exploiting the Logic BIST.   </p>  </div>  <div class="calendar-item">   <div class="header-wrapper">    <h3 class="calendar-paperheader">     Time Guarantee and Reliable Execution for Safety-Critical Real-Time Systems    </h3>    <div class="pdficon filter-blue">     <a href="/index.php/download?filename=PS2-3.pdf" target="_blank">      <img src="/files/pdficon.svg"/>     </a>    </div>   </div>   <div class="calendar-authors">    <p>     <b>      Authors:     </b>     Pegdwende Romaric Nikiema, Angeliki Kritikakou, Marcello Traiola, Olivier Sentieys    </p>   </div>   <div class="calendar-affiliations">    <p>     <b>      Affiliation:     </b>     University of Rennes (FR)    </p>   </div>   <input class="abstract-toggle" id="abstract-toggle-4" type="checkbox"/>   <label class="collapsible" for="abstract-toggle-4">    <b>     Abstract:    </b>   </label>   <div class="calendar-abstract abstract-content">    <p align="justify">     Safety-critical real-time embedded systems must guarantee both reliable and in-time execution. The increasing complexity of embedded systems and the reduction in technology size make modern processors more vulnerable to faults, especially in multicore systems, where it’s difficult to predict the timing behavior due to numerous interference. Faults impact not only the functional correctness of the application but also the timing correctness, which is paramount for safety-critical systems. The goal is to propose means to evaluate systems in the presence of faults and propose techniques that mitigate the errors to allow a safe execution. For the first step discussed in the following, we enhance vulnerability analysis to include functional and timing correctness, show that faults impact Worst-Case Execution Time (WCET) estimations, and propose a fault tolerance technique.    </p>   </div>   <input class="abstract-toggle" id="biography-toggle-4" type="checkbox"/>   <label class="collapsible" for="biography-toggle-4">    <b>     Biography:    </b>   </label>   <div class="calendar-abstract abstract-content">    <p align="justify">     Pegdwende Romaric NIKIEMA is a 2nd year Ph.D student at Université de Rennes, France. He received his B.S. degree in Electronic Electrotechnic and Control System from the Université Aube Nouvelle, Burkina Faso in 2020, his M.S. degree in Wireless Embedded Technology from the Nantes Université, France in 2022. His research interests include computer architectures, Real-time and dependable embedded systems.    </p>   </div>   <div class="calendar-item">    <div class="header-wrapper">     <h3 class="calendar-paperheader">      Irradiation Tests: Deriving Memory Design Parameters     </h3>     <div class="pdficon filter-blue">      <a href="/index.php/download?filename=PS2-4.pdf" target="_blank">       <img src="/files/pdficon.svg"/>      </a>     </div>    </div>    <div class="calendar-authors">     <p>      <b>       Authors:      </b>      N. Kolahimahmoudi, P. Bernardi     </p>    </div>    <div class="calendar-affiliations">     <p>      <b>       Affiliation:      </b>      Politecnico di Torino (IT)     </p>    </div>    <input class="abstract-toggle" id="abstract-toggle-5" type="checkbox"/>    <label class="collapsible" for="abstract-toggle-5">     <b>      Abstract:     </b>    </label>    <div class="calendar-abstract abstract-content">     <p align="justify">      This paper introduces a new method to derive architectural details from embedded System-on-Chip(SoC) memories. This method can extract memory design configurations (MDCs) such as mirroring, and scrambling utilizing Multiple Cell Upsets (MCUs) generated through a single irradiation test. Discovering the correct MDC corroborates the proposed method although, this method may find redundant MDCs alongside the correct MDC. The number of these redundant MDCs decreased with the increment of the MCUs. This number decreased to an average of 2 possible MDCs when considering 100 MCUs.     </p>    </div>    <input class="abstract-toggle" id="biography-toggle-5" type="checkbox"/>    <label class="collapsible" for="biography-toggle-5">     <b>      Biography:     </b>    </label>    <div class="calendar-abstract abstract-content">     <p align="justify">      Nima Kolahimahmoudi received his master’s in Electronic Engineering at the Polytechnic of Turin in 2022. Currently, he is a Ph.D. student in the Department of Control and Computer Engineering in the CAD and Reliability group under the supervision of Professor Paolo Bernardi. Mainly, his research targets the test and reliability of Automotive Systems-on-Chips, including analog and mixed-signal circuits and embedded memories.     </p>    </div>    <div class="calendar-item">     <div class="header-wrapper">      <h3 class="calendar-paperheader">       A Novel Machine Learning-based Fault Shape Classification for Memories Embedded In Automotive Systems-on-Chip      </h3>      <div class="pdficon filter-blue">       <a href="/index.php/download?filename=PS2-5.pdf" target="_blank">        <img src="/files/pdficon.svg"/>       </a>      </div>     </div>     <div class="calendar-authors">      <p>       <b>        Authors:       </b>       P. Bernardi, G. Insinga      </p>     </div>     <div class="calendar-affiliations">      <p>       <b>        Affiliation:       </b>       Politecnico di Torino (IT)      </p>     </div>     <input class="abstract-toggle" id="abstract-toggle-6" type="checkbox"/>     <label class="collapsible" for="abstract-toggle-6">      <b>       Abstract:      </b>     </label>     <div class="calendar-abstract abstract-content">      <p align="justify">       A significant percentage of modern Automotive System-on-Chip dies is occupied by embedded memories. Embedded memories have thus a high impact on the yield of these devices, and their testing and reliable operations are then key priorities for the manufacturers. Embedded memories are tested with a complete suite of tests that verify the correct behavior of the bit cells in various conditions such as supply voltage, operation frequency, temperature, etc. All these tests generate huge quantities of data that are difficult to analyze and to elaborate in an easily understandable form. A useful approach is to divide the failures based on their fault shapes. For example, a series of adjacent bitlines is probably related to a sense amplifier failure, or a series of adjacent wordlines is probably related to a failing minisector. Heuristic programs to recognize these shapes exist, but they are slow, not flexible, and prone to mistakes. In this work, we propose a new shape analysis solution that uses machine learning to recognize fault shapes faster and more reliably. To reach our goals, we trained a convolutional neural network (CNN) called ResNet18. This net is very famous in the literature for having high performance and being relatively lightweight. It takes its name from the 18 layers that compose it and accepts image tiles of 224x224 pixels and outputs the label of the recognized fault shape. The complete flow includes a preprocessing phase that prepares the input for the neural network and a post-processing phase that combines the output of the neural network to recognize bigger shapes. Experimental results on an Automotive-grade Infineon SoC show the validity of the approach, which correctly recognizes 97.9% of the failure on a set of 8000 failing segments.      </p>     </div>     <input class="abstract-toggle" id="biography-toggle-6" type="checkbox"/>     <label class="collapsible" for="biography-toggle-6">      <b>       Biography:      </b>     </label>     <div class="calendar-abstract abstract-content">      <p align="justify">       Giorgio Insinga received his master's degree in Electronics Engineering at Politecnico di Torino in 2021. He is a Ph.D. student at Politecnico di Torino under the supervision of professor Paolo Bernardi. His primary research focuses on the test and reliability of Automotive Systems-on-Chip and their embedded memories.      </p>     </div>     <div class="calendar-item">      <div class="header-wrapper">       <h3 class="calendar-paperheader">        Exploring Side Channel Attacks on Cutting-Edge Adder-Free SRAM CIM       </h3>       <div class="pdficon filter-blue">        <a href="/index.php/download?filename=PS2-6.pdf" target="_blank">         <img src="/files/pdficon.svg"/>        </a>       </div>      </div>      <div class="calendar-authors">       <p>        <b>         Authors:        </b>        Fouwad Jamil Mir, Abdullah Aljuffri, Mottaqiallah Taouil       </p>      </div>      <div class="calendar-affiliations">       <p>        <b>         Affiliation:        </b>        Delft University of Technology (NL)       </p>      </div>      <input class="abstract-toggle" id="abstract-toggle-7" type="checkbox"/>      <label class="collapsible" for="abstract-toggle-7">       <b>        Abstract:       </b>      </label>      <div class="calendar-abstract abstract-content">       <p align="justify">        A novel adder-free SRAM-based Computation-in-Memory (CIM) accelerator for Binary Neural Networks (BNNs) is susceptible to power side-channel attacks. This work proposes a framework to exploit the CIM periphery’s power signature to reverse-engineer SRAM cell weights. The methodology isolates the counter’s power signature, segments the power trace, and profiles these segments to identify weight values. Initial findings based on power profile analysis demonstrate the feasibility of weight information extraction. This work highlights the importance of security in CIM design and motivates research on countermeasures like masking or obfuscation to mitigate power side-channel vulnerabilities. Our contribution is a novel attack approach for digital CIM devices, paving the way for future research in securing these systems.       </p>      </div>      <input class="abstract-toggle" id="biography-toggle-7" type="checkbox"/>      <label class="collapsible" for="biography-toggle-7">       <b>        Biography:       </b>      </label>      <div class="calendar-abstract abstract-content">       <p align="justify">        Fouwad Mir is a Ph.D. candidate in Computer Engineering at the faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Delft University of Technology (TU-Delft). His research focuses on hardware security for novel computer architectures and neural network accelerators. He is enthusiastic about exploring hardware security vulnerabilities in digital and mixed-signal designs, aiming to develop mitigation strategies for robust and secure hardware.       </p>      </div>      <div class="calendar-item">       <div class="header-wrapper">        <h3 class="calendar-paperheader">         Pre-Silicon Fuzzing of RISC-V Hardware Components and their Interactions        </h3>        <div class="pdficon filter-blue">         <a href="/index.php/download?filename=PS2-7.pdf" target="_blank">          <img src="/files/pdficon.svg"/>         </a>        </div>       </div>       <div class="calendar-authors">        <p>         <b>          Authors:         </b>         Gijs Burghoorn, Abdullah Aljuffri, Mottaqiallah Taouil        </p>       </div>       <div class="calendar-affiliations">        <p>         <b>          Affiliation:         </b>         Delft University of Technology (NL)        </p>       </div>       <input class="abstract-toggle" id="abstract-toggle-8" type="checkbox"/>       <label class="collapsible" for="abstract-toggle-8">        <b>         Abstract:        </b>       </label>       <div class="calendar-abstract abstract-content">        <p align="justify">         In recent years, processor microarchitectures have shown both functional and security issues. Current mitigation-focused research centers around undirected testing of processors, i.e. hardware or processor fuzz testing. More recent promising studies focus on using test programs as fuzz inputs. Cascade, which is the state-of-the-art in these test program focused tools, uses a loosely integrated collection of existing tools. This severely limits what intricate behaviors and chip-component interactions it can test and the speed at which it can generate test programs. We propose a set of tightly integrated tools that allow for the real-time program generation. These tools allow for automation in the behavioral verification and security analysis of intricate interactions between chip-components.        </p>       </div>       <input class="abstract-toggle" id="biography-toggle-8" type="checkbox"/>       <label class="collapsible" for="biography-toggle-8">        <b>         Biography:        </b>       </label>       <div class="calendar-abstract abstract-content">        <p align="justify">         Gijs Burghoorn finished a BSc. Computer Science at Leiden University in the Netherlands. Afterwards, he went to Grenoble in France and got a MSc. in Informatics and Cybersecurity. Here, he performed research into Hardware Security, Side-Channel Analysis and Fault Injection. Afterwards, he started his PhD at the TU Delft in the Netherlands.        </p>       </div>       <div class="calendar-item">        <div class="header-wrapper">         <h3 class="calendar-paperheader">          Online Detection of Unique Faults in RRAMs         </h3>         <div class="pdficon filter-blue">          <a href="/index.php/download?filename=PS2-8.pdf" target="_blank">           <img src="/files/pdficon.svg"/>          </a>         </div>        </div>        <div class="calendar-authors">         <p>          <b>           Authors:          </b>          Hanzhi Xun          <sup>           1          </sup>          , Moritz Fieback          <sup>           1          </sup>          , Mohammad Amin Yaldagard          <sup>           1          </sup>          , Sicong Yuan          <sup>           1          </sup>          , Hassen Aziza          <sup>           2          </sup>          , Mottaqiallah Taouil          <sup>           1, 3          </sup>          , Said Hamdioui          <sup>           1, 3          </sup>         </p>        </div>        <div class="calendar-affiliations">         <p>          <b>           Affiliations:          </b>          <sup>           1          </sup>          Delft University of Technology (NL),          <sup>           2          </sup>          Aix-Marseille University (FR),          <sup>           3          </sup>          CognitiveIC (NL)         </p>        </div>        <input class="abstract-toggle" id="abstract-toggle-9" type="checkbox"/>        <label class="collapsible" for="abstract-toggle-9">         <b>          Abstract:         </b>        </label>        <div class="calendar-abstract abstract-content">         <p align="justify">          Due to the immature manufacturing process, Resistive Random Access Memories (RRAMs) are prone to exhibitnew failure mechanisms and faults, which should be efficiently detected for high-volume production. Those unique faults are hard to detect but require specific Design-for-Test (DfT) circuit design. This paper proposes a DfT based on a parallel-reference write circuit that can detect all RRAM array faults during diagnosis, production testing, and its application in the field.         </p>        </div>        <input class="abstract-toggle" id="biography-toggle-9" type="checkbox"/>        <label class="collapsible" for="biography-toggle-9">         <b>          Biography:         </b>        </label>        <div class="calendar-abstract abstract-content">         <p align="justify">          Hanzhi Xun received the B.Sc. and M.Eng. degrees from Xidian University, Xi'an, China, in 2018 and 2021, respectively. He received another M.Eng. degree form Waseda University, Kitakyushu, Japan, in 2021. He is currently working toward the Ph.D. degree at the Computer Engineering Laboratory, Delft University of Technology, Delft, The Netherlands. His research interests focus on device modeling, test, and reliability of Resistive RAMs.         </p>        </div>        <div class="calendar-item">         <div class="header-wrapper">          <h3 class="calendar-paperheader">           Reliability Assessment and Optimization of Dynamic DNNs for Edge Accelerators          </h3>          <div class="pdficon filter-blue">           <a href="/index.php/download?filename=PS2-9.pdf" target="_blank">            <img src="/files/pdficon.svg"/>           </a>          </div>         </div>         <div class="calendar-authors">          <p>           <b>            Authors:           </b>           Georgios Konstantinidis, Maria K. Michail, Theocharis Theocharides          </p>         </div>         <div class="calendar-affiliations">          <p>           <b>            Affiliation:           </b>           University of Cyprus (CY)          </p>         </div>         <input class="abstract-toggle" id="abstract-toggle-10" type="checkbox"/>         <label class="collapsible" for="abstract-toggle-10">          <b>           Abstract:          </b>         </label>         <div class="calendar-abstract abstract-content">          <p align="justify">           The urge to deploy Machine Learning (ML) algo- rithms for inference on edge devices, has created the need for efficient, resource-constrained and dependable hardware, that can be utilized according to the constraints of the underlying hardware as well as those imposed by the running applications. Recently, various types of dynamic deep neural networks have been introduced, mainly for the purpose of optimizing inference performance for real-time response systems. Such edge-based systems are often resource-constrained and are deployed in various safety-critical and/or harsh environments. Therefore, these systems have increased needs in reliability, while at the same time struggle to satisfy performance and energy requirements. This thesis will first investigate the vulnerability assessment of various dynamic deep neural networks, in order to better understand the impact of these performance optimizations on the overall reliability of the networks. Consequently, the thesis will propose various approaches to co-optimize the network design based on both performance and reliability targets. The proposed approaches will be evaluated within a newly developed simulation framework which allows for efficient yet accurate vulnerability analysis, as it considers hardware-aware software fault models.          </p>         </div>         <input class="abstract-toggle" id="biography-toggle-10" type="checkbox"/>         <label class="collapsible" for="biography-toggle-10">          <b>           Biography:          </b>         </label>         <div class="calendar-abstract abstract-content">          <p align="justify">           Georgios Konstantinidis received his BSc degree in Electrical and Electronic Engineering from the Department of Electrical and Computer Engineering at the University of Cyprus and his MSc degree in Mechatronics from the department of Mechanical Engineering at the University of Leeds. He is currently a PhD student. His research interests include Machine Learning, Embedded Systems, Forecasting Algorithms and ML Accelerators.          </p>         </div>         <div class="calendar-item">          <div class="header-wrapper">           <h3 class="calendar-paperheader">            Dependable Reconfigurable Scan Networks           </h3>           <div class="pdficon filter-blue">            <a href="/index.php/download?filename=PS2-10.pdf" target="_blank">             <img src="/files/pdficon.svg"/>            </a>           </div>          </div>          <div class="calendar-authors">           <p>            <b>             Author:            </b>            Natalia Lylina           </p>          </div>          <div class="calendar-affiliations">           <p>            <b>             Affiliation:            </b>            University of Stuttgart (DE)           </p>          </div>          <input class="abstract-toggle" id="abstract-toggle-12" type="checkbox"/>          <label class="collapsible" for="abstract-toggle-12">           <b>            Abstract:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            Today, dependable modern devices are equipped with an increasing number of extra-functional instrument to facilitate cost-efficient bring-up, debug, test, diagnosis, and adaptivity in the field and might include sensors, aging monitors and Built-In Self-Test (BIST) registers e.g. Reconfigurable Scan Networks (RSNs) provide a flexible way to access such instruments as well the device’s registers throughout the lifetime, starting from post-silicon validation (PSV) through manufacturing test and finally during in-field operation. At the same time, the dependability properties of the system can be affected through an improper RSN integration. This doctoral thesis overcomes these problems and establishes a unified method for design automation of dependable RSNs. The developed method considers the most relevant dependability aspects as robustness, testability, and security compliance.           </p>          </div>          <input class="abstract-toggle" id="biography-toggle-12" type="checkbox"/>          <label class="collapsible" for="biography-toggle-12">           <b>            Biography:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            Natalia Lylina received an M.Sc. double degree in computer science from Moscow Power Engineering Institute, Russia, and Technical University of Ilmenau, Germany. Since 2017, she has been with the Institute ofComputer Architecture and Computer Engineering at the University of Stuttgart as a PhD student, and received her doctoral title (Dr. rer. nat.) in 2022. She is a Member of IEEE. Her research interests include dependable systems, test and diagnosis, and reconfigurable scan networks.           </p>          </div>          <div class="calendar-item">           <div class="header-wrapper">            <h3 class="calendar-paperheader">             Toward Fault-Tolerant Applications on Reconfigurable Systems-on-Chip            </h3>            <div class="pdficon filter-blue">             <a href="/index.php/download?filename=PS2-11.pdf" target="_blank">              <img src="/files/pdficon.svg"/>             </a>            </div>           </div>           <div class="calendar-authors">            <p>             <b>              Authors:             </b>             Corrado De Sio, Luca Sterpone            </p>           </div>           <div class="calendar-affiliations">            <p>             <b>              Affiliation:             </b>             Politecnico di Torino (IT)            </p>           </div>           <input class="abstract-toggle" id="abstract-toggle-13" type="checkbox"/>           <label class="collapsible" for="abstract-toggle-13">            <b>             Abstract:            </b>           </label>           <div class="calendar-abstract abstract-content">            <p align="justify">             FPGA-based System-on-Chips (SoCs) have facilitated the integration of software programmability andcustom hardware acceleration. The research work focuses on accurate and efficient robustness analysis for Reconfigurable SoCs, considering their heterogeneous components and radiation induced effects. Research work includes the characterization of components, such as memories, and the comparison of differenttechnology processes through radiation testing experiments. Novel methodologies for reliability analysis of reconfigurable systems are proposed based on programmable hardware, supporting automation, integration with the development flow, and improving understanding of the fault affecting the system. The analysis of different components, such as soft and hard processors, host-device interfacing systems, and custom hardware accelerators, is presented, considering different fault models and dedicated evaluation approaches. Research work ranges from the hardware systems to the software stack of reconfigurable SoCs, providing a comprehensive investigation of their heterogeneity and the advantages that this can provide.            </p>           </div>           <input class="abstract-toggle" id="biography-toggle-13" type="checkbox"/>           <label class="collapsible" for="biography-toggle-13">            <b>             Biography:            </b>           </label>           <div class="calendar-abstract abstract-content">            <p align="justify">             CORRADO DE SIO received the M.S. degree in Computer Engineering from the University of Pisa. He received his Ph.D. from Politecnico di Torino in 2023. Currently, He works in the CAD &amp; Reliability group of Politecnico di Torino as a PostDoC . His research interests include reconfigurable devices, radiation effects, and EDA tools for analyzing and improving the reliability and the design of embedded and reconfigurable systems applications.            </p>           </div>           <div class="calendar-item">            <div class="header-wrapper">             <h3 class="calendar-paperheader">              SDfT: Secure Design for Testability             </h3>             <div class="pdficon filter-blue">              <a href="/index.php/download?filename=PS2-12.pdf" target="_blank">               <img src="/files/pdficon.svg"/>              </a>             </div>            </div>            <div class="calendar-authors">             <p>              <b>               Authors:              </b>              Yogendra Sao, Sk. Subidh Ali             </p>            </div>            <div class="calendar-affiliations">             <p>              <b>               Affiliation:              </b>              Indian Institute of Technology Bhilai (IN)             </p>            </div>            <input class="abstract-toggle" id="abstract-toggle-14" type="checkbox"/>            <label class="collapsible" for="abstract-toggle-14">             <b>              Abstract:             </b>            </label>            <div class="calendar-abstract abstract-content">             <p align="justify">              Scan-based design for Testability is the de-facto standard for chip testing, which provides high observability and test coverage by enabling direct access to chip memory elements. The scan-based Design-for-Testability (DfT) technique has also become the prime target of attackers whose aim is to extract the secret information embedded inside a chip by misusing its scan infrastructure. This thesis work performs a detailed security analysis (hardware vulnerability analysis and penetration testing) of existing defense mechanisms, discovers new vulnerabilities, and proposes a new defense mechanism against scan attacks.             </p>            </div>            <input class="abstract-toggle" id="biography-toggle-14" type="checkbox"/>            <label class="collapsible" for="biography-toggle-14">             <b>              Biography:             </b>            </label>            <div class="calendar-abstract abstract-content">             <p align="justify">              Yogendra Sao received an M.Tech. in computer science and engineering from the International Institute of Information Technology Hyderabad, India, in 2011 and a Ph.D. in computer science and engineering from the Indian Institute of Technology Bhilai in 2023.He is an Assistant Engineer with Chhattisgarh State Power Distribution Company Limited, Raipur, since 2011. His research interests include hardware security and secure design for testability. He has published 9 conference and journal papers.             </p>            </div>            <div class="calendar-item">             <div class="header-wrapper">              <h3 class="calendar-paperheader">               Design for Advanced Optical Test for Image and Photonic Sensors              </h3>              <div class="pdficon filter-blue">               <a href="/index.php/download?filename=PS2-13.pdf" target="_blank">                <img src="/files/pdficon.svg"/>               </a>              </div>             </div>             <div class="calendar-authors">              <p>               <b>                Authors:               </b>               J. Lefevre               <sup>                1, 2               </sup>               , P. Debaud               <sup>                1               </sup>               , P. Girard               <sup>                2               </sup>               , A. Virazel               <sup>                2               </sup>              </p>             </div>             <div class="calendar-affiliations">              <p>               <b>                Affiliations:               </b>               <sup>                1               </sup>               STMicroelectronics (FR),               <sup>                2               </sup>               LIRMM University of Montpellier / CNRS (FR)              </p>             </div>             <input class="abstract-toggle" id="abstract-toggle-15" type="checkbox"/>             <label class="collapsible" for="abstract-toggle-15">              <b>               Abstract:              </b>             </label>             <div class="calendar-abstract abstract-content">              <p align="justify">               No Abstract              </p>             </div>             <input class="abstract-toggle" id="biography-toggle-15" type="checkbox"/>             <label class="collapsible" for="biography-toggle-15">              <b>               Biography:              </b>             </label>             <div class="calendar-abstract abstract-content">              <p align="justify">               Julia Lefevre completed her PhD at STMicroelectronics Grenoble and the LIRMM (Laboratory of Informatic, Robotic and Microelectronics of Montpellier) in France. She graduated a master degree in microelectronic at the University of Montpellier in 2020 before to work on CMOS image sensor test, looking for innovative test techniques during her PhD. She is now working as a product engineer in the Imaging Division at STMicroelectronics and will defend her PhD in June.              </p>             </div>            </div>           </div>          </div>         </div>        </div>       </div>      </div>     </div>    </div>   </div>  </div> </div></div>
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DESCRIPTION:\N \N  \N   Moderator:\N  \N  Anteneh Gebregiorgis\N \N\N\N \N  \N   Affiliation:\N  \N  Delft University of Technology (NL)\N \N\N\N \N  \N   Modeling Thermal Effects For Biasing PUFs\N  \N  \N   \N    \N   \N  \N \N \N  \N   \N    Authors:\N   \N   Aghiles Douadi\N   \N    1\N   \N   , Elena-Ioana Vatajelu\N   \N    1\N   \N   , Paolo Maistri\N   \N    1\N   \N   , David Hely\N   \N    2\N   \N   , Vincent Beroulle\N   \N    2\N   \N   , Giorgio Di Natale\N   \N    1\N   \N  \N \N \N  \N   \N    Affiliations:\N   \N   \N    1\N   \N   University of Grenoble Alpes,CNRS (FR),\N   \N    2\N   \N   University of Grenoble Alpes (FR)\N  \N \N \N \N  \N   Abstract:\N  \N \N \N  \N   Security primitives such as Physical Unclonable Functions (PUFs) or True Random Number Generators (TRNGs), have emerged as hardware roots of trust for ensuring the security of modern applications. However, these primitives display susceptibility to physical attacks, among them, in the face of temperature variations. Previous research has established the feasibility of attacks exploiting temperature fluctuations to compromise the security of these primitives. Specifically, when implemented on FPGAs, programmable components can be vulnerable to alterations induced by thermal changes. These findings underscore the need to deepen the understanding of the implications of temperature sensitivity on the security and robustness of these security mechanisms. This paper studies how heat affects, both instantaneously and permanently, the working of ring oscillators, which are the building blocks of PUFs based on Ring Oscillators. The study also suggests how to exploit these effects to bias the PUf responses, enabling thus the possibility of its cloning.\N  \N \N \N  \N   \N    Post-Manufacture Criticality-Aware Gain Tuning of Timing Encoded Spiking Neural Networks for Yield Recovery\N   \N   \N    \N     \N    \N   \N  \N  \N   \N    \N     Authors:\N    \N    Anurup Saha, Kwondo Ma, Chandramouli Amarnath, Abhijit Chatterjee\N   \N  \N  \N   \N    \N     Affiliation:\N    \N    Georgia Institute of Technology (US)\N   \N  \N  \N  \N   \N    Abstract:\N   \N  \N  \N   \N    Time-to-first-spike (TTFS) encoded spiking neural networks (SNNs), implemented using memristive crossbar arrays (MCA), achieve higher inference speed and energy efficiency compared to artificial neural networks (ANNs) and rate encoded SNNs. However, memristive crossbar arrays are vulnerable to conductance variations in the embedded memristor cells. These degrade the performance of TTFS encoded SNNs, namely their classification accuracy, with adverse impact on the yield of manufactured chips. To combat this yield loss, we propose a post-manufacture testing and tuning framework for these SNNs. In the testing phase, a timing encoded signature of the SNN, which is statistically correlated to the SNN performace, is extracted. In the tuning phase, this signature is mapped to optimal values of the tuning knobs (gain parameters), one parameter per layer, using a trained regressor, allowing very fast tuning (about 150ms). To further reduce the tuning overhead, we rank order hidden layer neurons based on their criticality and show that adding gain programmability only to 50% of the neurons is sufficient for performance recovery. Experiments show that the proposed framework can improve yield by up to 34% and average accuracy of memristive SNNs by up to 9%.\N   \N  \N  \N   \N    \N     Extracting Weights of CIM-Based Neural Networks Through Power Analysis on Adder-Tree\N    \N    \N     \N      \N     \N    \N   \N   \N    \N     \N      Authors:\N     \N     Fouwad Mir, Abdullah AljuYri, Said Hamdioui, Mottaqiallah Taouil\N    \N   \N   \N    \N     \N      Affiliation:\N     \N     Delft University of Technology (NL)\N    \N   \N   \N   \N    \N     Abstract:\N    \N   \N   \N    \N     Computation-in-Memory (CIM) architectures are a promising solution for addressing the pressing need of energy efficient artificial intelligence (AI) devices. SRAM-based digital CIM architectures present a viable approach for enhancing the performance of machine learning algorithms, circumventing the intricacies of analog computing. Recent studies have revealed potential weaknesses in these architectures, particularly against power attacks. This study introduces a novel attack method enabling weight extraction through the analysis of the adder tree component within the architecture. In our attack, the k- means clustering technique is employed to identify the hamming weights of the CIM weights. Subsequently, we correlate traces belonging to Hamming groups of known weights (e.g., the max Hamming weight) and the remaining groups in order to identify their weight values. As a case study, the attack was applied on SRAM CIM implementation based on 40nm TSMC technology. The results indicate that the weights stored in CIM crossbar can be retrieved with 100% accuracy purely by analyzing the power consumption.\N    \N   \N   \N    \N     \N      Relation Coverage: A New Paradigm for Hardware/Software Testing\N     \N     \N      \N       \N      \N     \N    \N    \N     \N      \N       Authors:\N      \N      Christoph Hazott, Daniel Große\N     \N    \N    \N     \N      \N       Affiliation:\N      \N      Institute for Complex Systems,Johannes Kepler University (AT)\N     \N    \N    \N    \N     \N      Abstract:\N     \N    \N    \N     \N      While the Hardware (HW) domain and the Software (SW) domain use the concept of coverage to measure the thoroughness of tests, there isn’t an established common metric that applies to both worlds. In this paper we make two major contributions: First, leveraging the abstraction of Virtual Prototypes (VPs), we unify HW/SW coverage by viewing the HW/SW system as a single model. This enables the measurement of structural HW/SW metrics like line, function, and branch coverage via a novel non-intrusive approach, where neither the VP (representing the HW) nor the SW requires any modification. Second, based on the unified HW/SW coverage, we introduce relation coverage. The innovation is that the user can define a relation between the frequency of executing lines in the SW and the execution count of corresponding lines of the HW model. This relation expresses expected behavior to be covered during testing. As a case study, we consider HW/SW testing of a Gyroscope sensor controlled by SW running on a RISC-V VP.\N     \N    \N    \N     \N      \N       Optimizing System-Level Test Program Generation via Genetic Programming\N      \N      \N       \N        \N       \N      \N     \N     \N      \N       \N        Authors:\N       \N       Denis Schwachhofer\N       \N        1\N       \N       , Francesco Angione\N       \N        2\N       \N       , Steffen Becker\N       \N        1\N       \N       , Stefan Wagner\N       \N        2, 3\N       \N       , Matthias Sauer\N       \N        4\N       \N       , Paolo Bernardi\N       \N        2\N       \N       , Ilia Polian\N       \N        1\N       \N      \N     \N     \N      \N       \N        Affiliations:\N       \N       \N        1\N       \N       University of Stuttgart (DE),\N       \N        2\N       \N       Politecnico di Torino (IT),\N       \N        3\N       \N       Technical University of Munich (DE),\N       \N        4\N       \N       Advantest Europe (DE)\N      \N     \N     \N     \N      \N       Abstract:\N      \N     \N     \N      \N       The rising complexity of integrated devices has led to new defect types and failure modes at the system level that are not detected by structural tests. System-Level Test (SLT) is another test step to combat this challenge. SLT is in charge of exercising system-level interactions between hardware components and software. Non-functional properties, e.g., temperature, play a major role in SLT. This work focuses on the automatic generation of assembly test programs for SLT that aim to indirectly maximize a particular non-functional property, for example, the temperature. It is based on two-step generation with genetic algorithms. First, a fast architectural simulation is used with the genetic algorithm to provide a structure for the test programs. Afterward, an additional generation is done on the hardware to optimize the initial register contents of the program. The case study for gathering experimental results is a super-scalar out-of-order RISC-V processor, the Berkeley Out-of-Order Machine (BOOM). Experimental results show that the two-step generation is more effective in converging to a better power-hungry test program than only using the power consumption as a fitness function for the genetic algorithm.\N      \N     \N     \N      \N       \N        Scan Design Using Unsupervised Machine Learning to Reduce Functional Timing and Area Impact\N       \N       \N        \N         \N        \N       \N      \N      \N       \N        \N         Authors:\N        \N        Sandeep Kumar Goel\N        \N         1\N        \N        , Ankita Patidar\N        \N         1\N        \N        , Frank Lee\N        \N         2\N        \N       \N      \N      \N       \N        \N         Affiliations:\N        \N        \N         1\N        \N        TSMC (US),\N        \N         2\N        \N        TSMC (TW)\N       \N      \N      \N      \N       \N        Abstract:\N       \N      \N      \N       \N        Scan design adversely affects design performance, including speed, power, and routing congestion. Scan partitioning and reordering are required to mitigate these effects. We present an unsupervised machine learning-based method for scan partitioning to reduce the total scan wire length and make scan chains as compact as possible. For scan partitioning, we use the K-Means clustering method and reorder flops in a scan chain using the Traveling Salesman Problem (TSP) algorithm. Experimental results for three CPU designs show that significant savings in real wire length (2-3%), as well as a reduction in timing impact (27%), can be achieved with the proposed method compared to the best case obtained by a commercial EDA flow. Additionally, the optimized scan stitching also helped improve Design Rule check (DRC) violations, which aids in design closure.\N       \N      \N      \N       \N        \N         Assessing the Effectiveness of Software-Based Self-Test Programs for Static Cell-Aware Test\N        \N        \N         \N          \N         \N        \N       \N       \N        \N         \N          Authors:\N         \N         Riccardo Cantoro\N         \N          1\N         \N         , Michelangelo Grosso\N         \N          2\N         \N         , Iacopo Guglielminetti\N         \N          2\N         \N         , Reza Khoshzaban\N         \N          1\N         \N         , Matteo Sonza Reorda\N         \N          1\N         \N        \N       \N       \N        \N         \N          Affiliations:\N         \N         \N          1\N         \N         Politecnico di Torino (IT),\N         \N          2\N         \N         STMicroelectronics (IT)\N        \N       \N       \N       \N        \N         Abstract:\N        \N       \N       \N        \N         Software-Based Self-Test (SBST) is vastly adopted as a hardware safety mechanism for the in-field test of safety-critical systems in the form of Software Test Libraries (STLs). Typically, an STL’s diagnostic coverage is evaluated on the stuck-at fault model. As various defect-oriented fault models exist and are used for manufacturing testing, such as the popular cell-aware test (CAT), there is a need to evaluate the effectiveness of SBST when such models are targeted. This work targets static CAT faults. We evaluated the fault coverage of open-available STLs for a RISC-V SoC. We used results stemming from stuck-at fault simulation and gate-exhaustive simulation to elaborate on the obtained results.\N        \N       \N       \N        \N         \N          AMS Test Stimulus Generation and Response Analysis Using Hyperdimensional Clustering: Minimizing Misclassification Rate\N         \N         \N          \N           \N          \N         \N        \N        \N         \N          \N           Authors:\N          \N          Suhasini Komarraju, Mohamed Mejri, Akhil Tammana, Gowsika Dharmaraj, Chandramouli N Amarnath, Abhijit Chatterjee\N         \N        \N        \N         \N          \N           Affiliation:\N          \N          Georgia Institute of Technology (US)\N         \N        \N        \N        \N         \N          Abstract:\N         \N        \N        \N         \N          Prevalent test strategies for analog/mixed-signal systems rely on either (a) prediction of device-under-test (DUT) design specifications from observed test responses to carefully crafted alternate test stimulus, or (b) detecting outliers from known optimized test response statistics of devices subjected to expected manufacturing process variations. In both of these test paradigms, misclassification of DUTs (false positives and false negatives) is not explicitly considered during test generation itself due to computational complexity, but rather based on post-test determination of test acceptance thresholds. In this paper, we propose a novel test generation approach based on hyperdimensional clustering, that explicitly targets DUT misclassification rate during test stimulus generation itself. The use of hyperdimensional vectors for clustering good and bad devices along with a set of simple vector operations for training and inference allows fast determination of misclassification rate within the test generation procedure itself. Experimental results show that the test generation times are reduced by 15X with significant improvements in DUT misclassification rate.\N         \N        \N        \N         \N          \N           Transcoders: A Better Alternative to Denoising Autoencoders\N          \N          \N           \N            \N           \N          \N         \N         \N          \N           \N            Authors:\N           \N           Pushpak Raj Gautam, Alex Orailoglu\N          \N         \N         \N          \N           \N            Affiliation:\N           \N           UC San Diego (US)\N          \N         \N         \N         \N          \N           Abstract:\N          \N         \N         \N          \N           Image denoising is a popular technique that is used to remove noise incurred due to hardware faults or noise carefully crafted by an attacker. Autoencoders are some of the most popular denoisers. Their ability to learn a distribution’s latent space helps them achieve this property, and they are generally good at it. However, they are known to fumble in a white-box threat model where an attacker knows everything about the victim classifier and its denoiser network including its architecture and hyperparameters. We show that this problem stems from the autoencoder’s learning goal. In this paper, we augment an autoencoder’s learning goal to conceive what we call transcoders. This modification forces the transcoder network to learn a function that is more adept at denoising a given image. Our results, evaluated on two datasets MNIST and CIFAR10, a slew of attacks, and two threat models gray-box and white-box, help us argue the following: given a denoising tool built using an autoencoder, one can update the learning goal of the autoencoder to that of a transcoder, and achieve a transcoder-based denoiser that is significantly better at handling both fault-induced and attack-induced noise.\N          \N         \N         \N          \N           \N            Secure AND Safe infrasTructures fOR cps in the compute continuuM (SANDSTORM)\N           \N           \N            \N             \N            \N           \N          \N          \N           \N            \N             Authors:\N            \N            Ernesto Sanchez, Stefano Di Carlo\N           \N          \N          \N           \N            \N             Affiliation:\N            \N            Politecnico di Torino (IT)\N           \N          \N          \N          \N           \N            Abstract:\N           \N          \N          \N           \N            This project is part of the  SEcurity and RIghts In the CyberSpace Exptended Partnership (SERICS)  (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU\N           \N          \N          \N          \N           \N            Biography:\N           \N          \N          \N           \N            \N             Ernesto Sanchez\N            \N            received a Ms.c. degree in electronic engineering from Universidad Javeriana, Bogota, Colombia, in 2000 and a Ph.D. in computer engineering from the Politecnico di Torino, Italy, in 2006, where he is currently an Associate Professor with the Department of Control and Computer Engineering. His research interests include microprocessor testing, hardware security, and DNN reliability.\N           \N          \N         \N         \N          \N           \N            Securing the third millennium’s cyber-CARs (SCAR)\N           \N           \N            \N             \N            \N           \N          \N          \N           \N            \N             Authors:\N            \N            Anil Bayram Gogebakan, Alessandro Savino, Stefano Di Carlo\N           \N          \N          \N           \N            \N             Affiliation:\N            \N            Politecnico di Torino (IT)\N           \N          \N          \N          \N           \N            Abstract:\N           \N          \N          \N           \N            This project is part of the  SEcurity and RIghts In the CyberSpace Exptended Partnership (SERICS)  (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU\N           \N          \N          \N          \N           \N            Biography:\N           \N          \N          \N           \N            \N             Anil Bayram Gogebakan\N            \N            received a bachelor's degree in Electronic Engineering from Bilkent University in Turkey. He is currently a Ms.C. student in Computer Engineering at Politecnico di Torino (Italy), working as a research assistant at the SMILIES research group.\N           \N          \N         \N         \N          \N           \N            Virtual Environment and Tool-Boxing for Trustworthy Development of RISC-V-Based Cloud Services (Vitamin-V)\N           \N           \N            \N             \N            \N           \N          \N          \N          \N           \N            Abstract:\N           \N          \N          \N           \N            This project is funded by the European Union under the Horizon Europe Program (Project number: 101093062)\N           \N          \N          \N          \N           \N            Biography:\N           \N          \N          \N           \N            \N             Cristiano Chenet\N            \N            is pursuing a Ph.D. in Computer Engineering at the Department of Control and Computer Engineering, Politecnico di Torino. His current research interest includes the interplay between cybersecurity and artificial intelligence.\N            \N            \N            \N             Enrico Magliano\N            \N            is pursuing a Ph.D. in Artificial Intelligence at the Department of Control and Computer Engineering, Politecnico di Torino. His current research interest includes the interplay between reliability and artificial intelligence.\N           \N          \N         \N         \N          \N           \N            NEUROmorphic energy-efficient secure accelerators based on Phase change materials aUgmented siLicon photonicS (NEUROPULS)\N           \N           \N            \N             \N            \N           \N          \N          \N           \N            \N             Authors:\N            \N            Stefano Di Carlo\N            \N             1\N            \N            , Dimitris Gizopoulos\N            \N             2\N            \N            , Fabio Pavanello\N            \N             1\N            \N            , Alessandro Savino\N            \N             1\N            \N           \N          \N          \N           \N            \N             Affiliations:\N            \N            \N             1\N            \N            Politecnico di Torino (IT),\N            \N             2\N            \N            University of Athens (GR),\N            \N             3\N            \N            CRNS (FR)\N           \N          \N          \N          \N           \N            Abstract:\N           \N          \N          \N           \N            This project is funded by the European Union under the Horizon Europe Program (Project number: 101093062)\N           \N          \N          \N          \N           \N            Biography:\N           \N          \N          \N           \N            \N             Stefano Di Carlo\N            \N            received the M.S. and Ph.D. degrees in computer engineering and information technology from Politecnico di Torino, Italy, in 1999 and 2003, respectively. Since 2021, he has been a Full Professor with the Department of Control and Computer Engineering, Politecnico di Torino. His research interests include a diverse range, including reliability analysis, FPGA design, neuromorphic computing, etc. With over 250 peer-reviewed publications in esteemed IEEE/ACM TRANSACTIONS, journals, and conference proceedings, he also contributes to the editorial board of top-tier journals. His involvement extends to serving on various organizing and program committees for major IEEE and ACM conferences and symposia. Notably, he is recognized as a Golden Core Member of the IEEE Computer Society and has received both Outstanding and Meritorious Awards for his volunteer efforts within the society.\N            \N            \N            \N             Alessandro\N            \N            Savino received the Ph.D. from Politecnico di Torino, Turin, Italy. He is an Associate Professor with the Department of Control and Computer Engineering, Politecnico di Torino. His research interests include approximate computing, reliability analysis, safety-critical systems, software-based self-tests, operating systems, imaging algorithms, machine learning, and audio manipulation.\N           \N          \N         \N         \N          \N           \N            Scaling Up Secure Processing, Anonymization And Generation Of Health Data For EU Cross Border Collaborative Research And Innovation (SECURED)\N           \N           \N            \N             \N            \N           \N          \N          \N          \N           \N            Abstract:\N           \N          \N          \N           \N            Big data is data that contains huge and hard-to-manage volumes of structured and unstructured data. It is so big that it is difficult or impossible to process using traditional methods. The EU-funded SECURED project will increase efficiency by scaling up multi-party computation, data anonymisation and synthetic data generation. Focusing on private and unbiased AI and data analytics, it will demonstrate technologies developed in health-related use cases like real-time tumour classification, telemonitoring for children and access to genomic data. In addition to speeding up nd facilitating privacy preserving data-driven tools and services for well-being, prevention, diagnosis, treatment and follow-up care, SECURED will also analyse the current ethical and legal hallenges to data sharing. The project is coordinated by Francesco Regazzoni from University of Amsterdam.\N           \N          \N         \N         \N          \N           \N            Multi-layer 360° dYnamic orchestration and interopeRable design environmenT for compute-continUum Systems (MYRTUS)\N           \N           \N            \N             \N            \N           \N          \N          \N          \N           \N            Abstract:\N           \N          \N          \N           \N            The MYRTUS project aims at unlocking the new living dimension of CPS, embracing the principles of the EU CloudEdgeIOT Initiative, integrating edge, fog and cloud computing platforms. This  ntegration requires the reinvention of programming languages and tools to orchestrate collaborative distributed and decentralised components. Additionally, components must be augmented with   nterface contracts covering both functional and non-functional properties. MYRTUS solutions play a crucial role in enabling sustainable computing and trustworthiness in CPS. The project is  oordinated by Katiuscia Zedda from Abinsula, while the scientific coordination is carried out by Francesca Palumbo from University of Cagliari.\N           \N          \N         \N        \N       \N      \N     \N    \N   \N  \N \N\N
X-ALT-DESC;FMTTYPE=text/html:<div class="calendar-authors"> <p>  <b>   Moderator:  </b>  Anteneh Gebregiorgis </p></div><div class="calendar-affiliations"> <p>  <b>   Affiliation:  </b>  Delft University of Technology (NL) </p></div><div class="calendar-item"> <div class="header-wrapper">  <h3 class="calendar-paperheader">   Modeling Thermal Effects For Biasing PUFs  </h3>  <div class="pdficon filter-red">   <a href="/index.php/download?filename=SEU-1.pdf" target="_blank">    <img src="/files/pdficon.svg"/>   </a>  </div> </div> <div class="calendar-authors">  <p>   <b>    Authors:   </b>   Aghiles Douadi   <sup>    1   </sup>   , Elena-Ioana Vatajelu   <sup>    1   </sup>   , Paolo Maistri   <sup>    1   </sup>   , David Hely   <sup>    2   </sup>   , Vincent Beroulle   <sup>    2   </sup>   , Giorgio Di Natale   <sup>    1   </sup>  </p> </div> <div class="calendar-affiliations">  <p>   <b>    Affiliations:   </b>   <sup>    1   </sup>   University of Grenoble Alpes,CNRS (FR),   <sup>    2   </sup>   University of Grenoble Alpes (FR)  </p> </div> <input class="abstract-toggle" id="abstract-toggle-2" type="checkbox"/> <label class="collapsible" for="abstract-toggle-2">  <b>   Abstract:  </b> </label> <div class="calendar-abstract abstract-content">  <p align="justify">   Security primitives such as Physical Unclonable Functions (PUFs) or True Random Number Generators (TRNGs), have emerged as hardware roots of trust for ensuring the security of modern applications. However, these primitives display susceptibility to physical attacks, among them, in the face of temperature variations. Previous research has established the feasibility of attacks exploiting temperature fluctuations to compromise the security of these primitives. Specifically, when implemented on FPGAs, programmable components can be vulnerable to alterations induced by thermal changes. These findings underscore the need to deepen the understanding of the implications of temperature sensitivity on the security and robustness of these security mechanisms. This paper studies how heat affects, both instantaneously and permanently, the working of ring oscillators, which are the building blocks of PUFs based on Ring Oscillators. The study also suggests how to exploit these effects to bias the PUf responses, enabling thus the possibility of its cloning.  </p> </div> <div class="calendar-item">  <div class="header-wrapper">   <h3 class="calendar-paperheader">    Post-Manufacture Criticality-Aware Gain Tuning of Timing Encoded Spiking Neural Networks for Yield Recovery   </h3>   <div class="pdficon filter-red">    <a href="/index.php/download?filename=SEU-2.pdf" target="_blank">     <img src="/files/pdficon.svg"/>    </a>   </div>  </div>  <div class="calendar-authors">   <p>    <b>     Authors:    </b>    Anurup Saha, Kwondo Ma, Chandramouli Amarnath, Abhijit Chatterjee   </p>  </div>  <div class="calendar-affiliations">   <p>    <b>     Affiliation:    </b>    Georgia Institute of Technology (US)   </p>  </div>  <input class="abstract-toggle" id="abstract-toggle-3" type="checkbox"/>  <label class="collapsible" for="abstract-toggle-3">   <b>    Abstract:   </b>  </label>  <div class="calendar-abstract abstract-content">   <p align="justify">    Time-to-first-spike (TTFS) encoded spiking neural networks (SNNs), implemented using memristive crossbar arrays (MCA), achieve higher inference speed and energy efficiency compared to artificial neural networks (ANNs) and rate encoded SNNs. However, memristive crossbar arrays are vulnerable to conductance variations in the embedded memristor cells. These degrade the performance of TTFS encoded SNNs, namely their classification accuracy, with adverse impact on the yield of manufactured chips. To combat this yield loss, we propose a post-manufacture testing and tuning framework for these SNNs. In the testing phase, a timing encoded signature of the SNN, which is statistically correlated to the SNN performace, is extracted. In the tuning phase, this signature is mapped to optimal values of the tuning knobs (gain parameters), one parameter per layer, using a trained regressor, allowing very fast tuning (about 150ms). To further reduce the tuning overhead, we rank order hidden layer neurons based on their criticality and show that adding gain programmability only to 50% of the neurons is sufficient for performance recovery. Experiments show that the proposed framework can improve yield by up to 34% and average accuracy of memristive SNNs by up to 9%.   </p>  </div>  <div class="calendar-item">   <div class="header-wrapper">    <h3 class="calendar-paperheader">     Extracting Weights of CIM-Based Neural Networks Through Power Analysis on Adder-Tree    </h3>    <div class="pdficon filter-red">     <a href="/index.php/download?filename=SEU-3.pdf" target="_blank">      <img src="/files/pdficon.svg"/>     </a>    </div>   </div>   <div class="calendar-authors">    <p>     <b>      Authors:     </b>     Fouwad Mir, Abdullah AljuYri, Said Hamdioui, Mottaqiallah Taouil    </p>   </div>   <div class="calendar-affiliations">    <p>     <b>      Affiliation:     </b>     Delft University of Technology (NL)    </p>   </div>   <input class="abstract-toggle" id="abstract-toggle-4" type="checkbox"/>   <label class="collapsible" for="abstract-toggle-4">    <b>     Abstract:    </b>   </label>   <div class="calendar-abstract abstract-content">    <p align="justify">     Computation-in-Memory (CIM) architectures are a promising solution for addressing the pressing need of energy efficient artificial intelligence (AI) devices. SRAM-based digital CIM architectures present a viable approach for enhancing the performance of machine learning algorithms, circumventing the intricacies of analog computing. Recent studies have revealed potential weaknesses in these architectures, particularly against power attacks. This study introduces a novel attack method enabling weight extraction through the analysis of the adder tree component within the architecture. In our attack, the k- means clustering technique is employed to identify the hamming weights of the CIM weights. Subsequently, we correlate traces belonging to Hamming groups of known weights (e.g., the max Hamming weight) and the remaining groups in order to identify their weight values. As a case study, the attack was applied on SRAM CIM implementation based on 40nm TSMC technology. The results indicate that the weights stored in CIM crossbar can be retrieved with 100% accuracy purely by analyzing the power consumption.    </p>   </div>   <div class="calendar-item">    <div class="header-wrapper">     <h3 class="calendar-paperheader">      Relation Coverage: A New Paradigm for Hardware/Software Testing     </h3>     <div class="pdficon filter-red">      <a href="/index.php/download?filename=SEU-4.pdf" target="_blank">       <img src="/files/pdficon.svg"/>      </a>     </div>    </div>    <div class="calendar-authors">     <p>      <b>       Authors:      </b>      Christoph Hazott, Daniel Große     </p>    </div>    <div class="calendar-affiliations">     <p>      <b>       Affiliation:      </b>      Institute for Complex Systems,Johannes Kepler University (AT)     </p>    </div>    <input class="abstract-toggle" id="abstract-toggle-5" type="checkbox"/>    <label class="collapsible" for="abstract-toggle-5">     <b>      Abstract:     </b>    </label>    <div class="calendar-abstract abstract-content">     <p align="justify">      While the Hardware (HW) domain and the Software (SW) domain use the concept of coverage to measure the thoroughness of tests, there isn’t an established common metric that applies to both worlds. In this paper we make two major contributions: First, leveraging the abstraction of Virtual Prototypes (VPs), we unify HW/SW coverage by viewing the HW/SW system as a single model. This enables the measurement of structural HW/SW metrics like line, function, and branch coverage via a novel non-intrusive approach, where neither the VP (representing the HW) nor the SW requires any modification. Second, based on the unified HW/SW coverage, we introduce relation coverage. The innovation is that the user can define a relation between the frequency of executing lines in the SW and the execution count of corresponding lines of the HW model. This relation expresses expected behavior to be covered during testing. As a case study, we consider HW/SW testing of a Gyroscope sensor controlled by SW running on a RISC-V VP.     </p>    </div>    <div class="calendar-item">     <div class="header-wrapper">      <h3 class="calendar-paperheader">       Optimizing System-Level Test Program Generation via Genetic Programming      </h3>      <div class="pdficon filter-red">       <a href="/index.php/download?filename=SEU-5.pdf" target="_blank">        <img src="/files/pdficon.svg"/>       </a>      </div>     </div>     <div class="calendar-authors">      <p>       <b>        Authors:       </b>       Denis Schwachhofer       <sup>        1       </sup>       , Francesco Angione       <sup>        2       </sup>       , Steffen Becker       <sup>        1       </sup>       , Stefan Wagner       <sup>        2, 3       </sup>       , Matthias Sauer       <sup>        4       </sup>       , Paolo Bernardi       <sup>        2       </sup>       , Ilia Polian       <sup>        1       </sup>      </p>     </div>     <div class="calendar-affiliations">      <p>       <b>        Affiliations:       </b>       <sup>        1       </sup>       University of Stuttgart (DE),       <sup>        2       </sup>       Politecnico di Torino (IT),       <sup>        3       </sup>       Technical University of Munich (DE),       <sup>        4       </sup>       Advantest Europe (DE)      </p>     </div>     <input class="abstract-toggle" id="abstract-toggle-6" type="checkbox"/>     <label class="collapsible" for="abstract-toggle-6">      <b>       Abstract:      </b>     </label>     <div class="calendar-abstract abstract-content">      <p align="justify">       The rising complexity of integrated devices has led to new defect types and failure modes at the system level that are not detected by structural tests. System-Level Test (SLT) is another test step to combat this challenge. SLT is in charge of exercising system-level interactions between hardware components and software. Non-functional properties, e.g., temperature, play a major role in SLT. This work focuses on the automatic generation of assembly test programs for SLT that aim to indirectly maximize a particular non-functional property, for example, the temperature. It is based on two-step generation with genetic algorithms. First, a fast architectural simulation is used with the genetic algorithm to provide a structure for the test programs. Afterward, an additional generation is done on the hardware to optimize the initial register contents of the program. The case study for gathering experimental results is a super-scalar out-of-order RISC-V processor, the Berkeley Out-of-Order Machine (BOOM). Experimental results show that the two-step generation is more effective in converging to a better power-hungry test program than only using the power consumption as a fitness function for the genetic algorithm.      </p>     </div>     <div class="calendar-item">      <div class="header-wrapper">       <h3 class="calendar-paperheader">        Scan Design Using Unsupervised Machine Learning to Reduce Functional Timing and Area Impact       </h3>       <div class="pdficon filter-red">        <a href="/index.php/download?filename=SEU-6.pdf" target="_blank">         <img src="/files/pdficon.svg"/>        </a>       </div>      </div>      <div class="calendar-authors">       <p>        <b>         Authors:        </b>        Sandeep Kumar Goel        <sup>         1        </sup>        , Ankita Patidar        <sup>         1        </sup>        , Frank Lee        <sup>         2        </sup>       </p>      </div>      <div class="calendar-affiliations">       <p>        <b>         Affiliations:        </b>        <sup>         1        </sup>        TSMC (US),        <sup>         2        </sup>        TSMC (TW)       </p>      </div>      <input class="abstract-toggle" id="abstract-toggle-7" type="checkbox"/>      <label class="collapsible" for="abstract-toggle-7">       <b>        Abstract:       </b>      </label>      <div class="calendar-abstract abstract-content">       <p align="justify">        Scan design adversely affects design performance, including speed, power, and routing congestion. Scan partitioning and reordering are required to mitigate these effects. We present an unsupervised machine learning-based method for scan partitioning to reduce the total scan wire length and make scan chains as compact as possible. For scan partitioning, we use the K-Means clustering method and reorder flops in a scan chain using the Traveling Salesman Problem (TSP) algorithm. Experimental results for three CPU designs show that significant savings in real wire length (2-3%), as well as a reduction in timing impact (27%), can be achieved with the proposed method compared to the best case obtained by a commercial EDA flow. Additionally, the optimized scan stitching also helped improve Design Rule check (DRC) violations, which aids in design closure.       </p>      </div>      <div class="calendar-item">       <div class="header-wrapper">        <h3 class="calendar-paperheader">         Assessing the Effectiveness of Software-Based Self-Test Programs for Static Cell-Aware Test        </h3>        <div class="pdficon filter-red">         <a href="/index.php/download?filename=SEU-7.pdf" target="_blank">          <img src="/files/pdficon.svg"/>         </a>        </div>       </div>       <div class="calendar-authors">        <p>         <b>          Authors:         </b>         Riccardo Cantoro         <sup>          1         </sup>         , Michelangelo Grosso         <sup>          2         </sup>         , Iacopo Guglielminetti         <sup>          2         </sup>         , Reza Khoshzaban         <sup>          1         </sup>         , Matteo Sonza Reorda         <sup>          1         </sup>        </p>       </div>       <div class="calendar-affiliations">        <p>         <b>          Affiliations:         </b>         <sup>          1         </sup>         Politecnico di Torino (IT),         <sup>          2         </sup>         STMicroelectronics (IT)        </p>       </div>       <input class="abstract-toggle" id="abstract-toggle-8" type="checkbox"/>       <label class="collapsible" for="abstract-toggle-8">        <b>         Abstract:        </b>       </label>       <div class="calendar-abstract abstract-content">        <p align="justify">         Software-Based Self-Test (SBST) is vastly adopted as a hardware safety mechanism for the in-field test of safety-critical systems in the form of Software Test Libraries (STLs). Typically, an STL’s diagnostic coverage is evaluated on the stuck-at fault model. As various defect-oriented fault models exist and are used for manufacturing testing, such as the popular cell-aware test (CAT), there is a need to evaluate the effectiveness of SBST when such models are targeted. This work targets static CAT faults. We evaluated the fault coverage of open-available STLs for a RISC-V SoC. We used results stemming from stuck-at fault simulation and gate-exhaustive simulation to elaborate on the obtained results.        </p>       </div>       <div class="calendar-item">        <div class="header-wrapper">         <h3 class="calendar-paperheader">          AMS Test Stimulus Generation and Response Analysis Using Hyperdimensional Clustering: Minimizing Misclassification Rate         </h3>         <div class="pdficon filter-red">          <a href="/index.php/download?filename=SEU-8.pdf" target="_blank">           <img src="/files/pdficon.svg"/>          </a>         </div>        </div>        <div class="calendar-authors">         <p>          <b>           Authors:          </b>          Suhasini Komarraju, Mohamed Mejri, Akhil Tammana, Gowsika Dharmaraj, Chandramouli N Amarnath, Abhijit Chatterjee         </p>        </div>        <div class="calendar-affiliations">         <p>          <b>           Affiliation:          </b>          Georgia Institute of Technology (US)         </p>        </div>        <input class="abstract-toggle" id="abstract-toggle-9" type="checkbox"/>        <label class="collapsible" for="abstract-toggle-9">         <b>          Abstract:         </b>        </label>        <div class="calendar-abstract abstract-content">         <p align="justify">          Prevalent test strategies for analog/mixed-signal systems rely on either (a) prediction of device-under-test (DUT) design specifications from observed test responses to carefully crafted alternate test stimulus, or (b) detecting outliers from known optimized test response statistics of devices subjected to expected manufacturing process variations. In both of these test paradigms, misclassification of DUTs (false positives and false negatives) is not explicitly considered during test generation itself due to computational complexity, but rather based on post-test determination of test acceptance thresholds. In this paper, we propose a novel test generation approach based on hyperdimensional clustering, that explicitly targets DUT misclassification rate during test stimulus generation itself. The use of hyperdimensional vectors for clustering good and bad devices along with a set of simple vector operations for training and inference allows fast determination of misclassification rate within the test generation procedure itself. Experimental results show that the test generation times are reduced by 15X with significant improvements in DUT misclassification rate.         </p>        </div>        <div class="calendar-item">         <div class="header-wrapper">          <h3 class="calendar-paperheader">           Transcoders: A Better Alternative to Denoising Autoencoders          </h3>          <div class="pdficon filter-red">           <a href="/index.php/download?filename=SEU-9.pdf" target="_blank">            <img src="/files/pdficon.svg"/>           </a>          </div>         </div>         <div class="calendar-authors">          <p>           <b>            Authors:           </b>           Pushpak Raj Gautam, Alex Orailoglu          </p>         </div>         <div class="calendar-affiliations">          <p>           <b>            Affiliation:           </b>           UC San Diego (US)          </p>         </div>         <input class="abstract-toggle" id="abstract-toggle-10" type="checkbox"/>         <label class="collapsible" for="abstract-toggle-10">          <b>           Abstract:          </b>         </label>         <div class="calendar-abstract abstract-content">          <p align="justify">           Image denoising is a popular technique that is used to remove noise incurred due to hardware faults or noise carefully crafted by an attacker. Autoencoders are some of the most popular denoisers. Their ability to learn a distribution’s latent space helps them achieve this property, and they are generally good at it. However, they are known to fumble in a white-box threat model where an attacker knows everything about the victim classifier and its denoiser network including its architecture and hyperparameters. We show that this problem stems from the autoencoder’s learning goal. In this paper, we augment an autoencoder’s learning goal to conceive what we call transcoders. This modification forces the transcoder network to learn a function that is more adept at denoising a given image. Our results, evaluated on two datasets MNIST and CIFAR10, a slew of attacks, and two threat models gray-box and white-box, help us argue the following: given a denoising tool built using an autoencoder, one can update the learning goal of the autoencoder to that of a transcoder, and achieve a transcoder-based denoiser that is significantly better at handling both fault-induced and attack-induced noise.          </p>         </div>         <div class="calendar-item">          <div class="header-wrapper">           <h3 class="calendar-paperheader">            Secure AND Safe infrasTructures fOR cps in the compute continuuM (SANDSTORM)           </h3>           <div class="pdficon">            <a href="https://serics.eu/en/progetti/" target="_blank">             <img src="/files/linkicon.svg" style="height: 24px;"/>            </a>           </div>          </div>          <div class="calendar-authors">           <p>            <b>             Authors:            </b>            Ernesto Sanchez, Stefano Di Carlo           </p>          </div>          <div class="calendar-affiliations">           <p>            <b>             Affiliation:            </b>            Politecnico di Torino (IT)           </p>          </div>          <input class="abstract-toggle" id="abstract-toggle-12" type="checkbox"/>          <label class="collapsible" for="abstract-toggle-12">           <b>            Abstract:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            This project is part of the  SEcurity and RIghts In the CyberSpace Exptended Partnership (SERICS)  (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU           </p>          </div>          <input class="abstract-toggle" id="biography-toggle-12" type="checkbox"/>          <label class="collapsible" for="biography-toggle-12">           <b>            Biography:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            <b>             Ernesto Sanchez            </b>            received a Ms.c. degree in electronic engineering from Universidad Javeriana, Bogota, Colombia, in 2000 and a Ph.D. in computer engineering from the Politecnico di Torino, Italy, in 2006, where he is currently an Associate Professor with the Department of Control and Computer Engineering. His research interests include microprocessor testing, hardware security, and DNN reliability.           </p>          </div>         </div>         <div class="calendar-item">          <div class="header-wrapper">           <h3 class="calendar-paperheader">            Securing the third millennium’s cyber-CARs (SCAR)           </h3>           <div class="pdficon">            <a href="https://serics.eu/en/progetti/" target="_blank">             <img src="/files/linkicon.svg" style="height: 24px;"/>            </a>           </div>          </div>          <div class="calendar-authors">           <p>            <b>             Authors:            </b>            Anil Bayram Gogebakan, Alessandro Savino, Stefano Di Carlo           </p>          </div>          <div class="calendar-affiliations">           <p>            <b>             Affiliation:            </b>            Politecnico di Torino (IT)           </p>          </div>          <input class="abstract-toggle" id="abstract-toggle-13" type="checkbox"/>          <label class="collapsible" for="abstract-toggle-13">           <b>            Abstract:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            This project is part of the  SEcurity and RIghts In the CyberSpace Exptended Partnership (SERICS)  (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU           </p>          </div>          <input class="abstract-toggle" id="biography-toggle-13" type="checkbox"/>          <label class="collapsible" for="biography-toggle-13">           <b>            Biography:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            <b>             Anil Bayram Gogebakan            </b>            received a bachelor's degree in Electronic Engineering from Bilkent University in Turkey. He is currently a Ms.C. student in Computer Engineering at Politecnico di Torino (Italy), working as a research assistant at the SMILIES research group.           </p>          </div>         </div>         <div class="calendar-item">          <div class="header-wrapper">           <h3 class="calendar-paperheader">            Virtual Environment and Tool-Boxing for Trustworthy Development of RISC-V-Based Cloud Services (Vitamin-V)           </h3>           <div class="pdficon">            <a href="https://www.vitamin-v.eu/" target="_blank">             <img src="/files/linkicon.svg" style="height: 24px;"/>            </a>           </div>          </div>          <input class="abstract-toggle" id="abstract-toggle-14" type="checkbox"/>          <label class="collapsible" for="abstract-toggle-14">           <b>            Abstract:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            This project is funded by the European Union under the Horizon Europe Program (Project number: 101093062)           </p>          </div>          <input class="abstract-toggle" id="biography-toggle-14" type="checkbox"/>          <label class="collapsible" for="biography-toggle-14">           <b>            Biography:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            <b>             Cristiano Chenet            </b>            is pursuing a Ph.D. in Computer Engineering at the Department of Control and Computer Engineering, Politecnico di Torino. His current research interest includes the interplay between cybersecurity and artificial intelligence.            <br/>            <br/>            <b>             Enrico Magliano            </b>            is pursuing a Ph.D. in Artificial Intelligence at the Department of Control and Computer Engineering, Politecnico di Torino. His current research interest includes the interplay between reliability and artificial intelligence.           </p>          </div>         </div>         <div class="calendar-item">          <div class="header-wrapper">           <h3 class="calendar-paperheader">            NEUROmorphic energy-efficient secure accelerators based on Phase change materials aUgmented siLicon photonicS (NEUROPULS)           </h3>           <div class="pdficon">            <a href="https://neuropuls.eu/" target="_blank">             <img src="/files/linkicon.svg" style="height: 24px;"/>            </a>           </div>          </div>          <div class="calendar-authors">           <p>            <b>             Authors:            </b>            Stefano Di Carlo            <sup>             1            </sup>            , Dimitris Gizopoulos            <sup>             2            </sup>            , Fabio Pavanello            <sup>             1            </sup>            , Alessandro Savino            <sup>             1            </sup>           </p>          </div>          <div class="calendar-affiliations">           <p>            <b>             Affiliations:            </b>            <sup>             1            </sup>            Politecnico di Torino (IT),            <sup>             2            </sup>            University of Athens (GR),            <sup>             3            </sup>            CRNS (FR)           </p>          </div>          <input class="abstract-toggle" id="abstract-toggle-15" type="checkbox"/>          <label class="collapsible" for="abstract-toggle-15">           <b>            Abstract:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            This project is funded by the European Union under the Horizon Europe Program (Project number: 101093062)           </p>          </div>          <input class="abstract-toggle" id="biography-toggle-15" type="checkbox"/>          <label class="collapsible" for="biography-toggle-15">           <b>            Biography:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            <b>             Stefano Di Carlo            </b>            received the M.S. and Ph.D. degrees in computer engineering and information technology from Politecnico di Torino, Italy, in 1999 and 2003, respectively. Since 2021, he has been a Full Professor with the Department of Control and Computer Engineering, Politecnico di Torino. His research interests include a diverse range, including reliability analysis, FPGA design, neuromorphic computing, etc. With over 250 peer-reviewed publications in esteemed IEEE/ACM TRANSACTIONS, journals, and conference proceedings, he also contributes to the editorial board of top-tier journals. His involvement extends to serving on various organizing and program committees for major IEEE and ACM conferences and symposia. Notably, he is recognized as a Golden Core Member of the IEEE Computer Society and has received both Outstanding and Meritorious Awards for his volunteer efforts within the society.            <br/>            <br/>            <b>             Alessandro            </b>            Savino received the Ph.D. from Politecnico di Torino, Turin, Italy. He is an Associate Professor with the Department of Control and Computer Engineering, Politecnico di Torino. His research interests include approximate computing, reliability analysis, safety-critical systems, software-based self-tests, operating systems, imaging algorithms, machine learning, and audio manipulation.           </p>          </div>         </div>         <div class="calendar-item">          <div class="header-wrapper">           <h3 class="calendar-paperheader">            Scaling Up Secure Processing, Anonymization And Generation Of Health Data For EU Cross Border Collaborative Research And Innovation (SECURED)           </h3>           <div class="pdficon">            <a href="https://secured-project.eu/" target="_blank">             <img src="/files/linkicon.svg" style="height: 24px;"/>            </a>           </div>          </div>          <input class="abstract-toggle" id="abstract-toggle-16" type="checkbox"/>          <label class="collapsible" for="abstract-toggle-16">           <b>            Abstract:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            Big data is data that contains huge and hard-to-manage volumes of structured and unstructured data. It is so big that it is difficult or impossible to process using traditional methods. The EU-funded SECURED project will increase efficiency by scaling up multi-party computation, data anonymisation and synthetic data generation. Focusing on private and unbiased AI and data analytics, it will demonstrate technologies developed in health-related use cases like real-time tumour classification, telemonitoring for children and access to genomic data. In addition to speeding up nd facilitating privacy preserving data-driven tools and services for well-being, prevention, diagnosis, treatment and follow-up care, SECURED will also analyse the current ethical and legal hallenges to data sharing. The project is coordinated by Francesco Regazzoni from University of Amsterdam.           </p>          </div>         </div>         <div class="calendar-item">          <div class="header-wrapper">           <h3 class="calendar-paperheader">            Multi-layer 360° dYnamic orchestration and interopeRable design environmenT for compute-continUum Systems (MYRTUS)           </h3>           <div class="pdficon">            <a href="https://myrtus-project.eu/" target="_blank">             <img src="/files/linkicon.svg" style="height: 24px;"/>            </a>           </div>          </div>          <input class="abstract-toggle" id="abstract-toggle-17" type="checkbox"/>          <label class="collapsible" for="abstract-toggle-17">           <b>            Abstract:           </b>          </label>          <div class="calendar-abstract abstract-content">           <p align="justify">            The MYRTUS project aims at unlocking the new living dimension of CPS, embracing the principles of the EU CloudEdgeIOT Initiative, integrating edge, fog and cloud computing platforms. This  ntegration requires the reinvention of programming languages and tools to orchestrate collaborative distributed and decentralised components. Additionally, components must be augmented with   nterface contracts covering both functional and non-functional properties. MYRTUS solutions play a crucial role in enabling sustainable computing and trustworthiness in CPS. The project is  oordinated by Katiuscia Zedda from Abinsula, while the scientific coordination is carried out by Francesca Palumbo from University of Cagliari.           </p>          </div>         </div>        </div>       </div>      </div>     </div>    </div>   </div>  </div> </div></div>
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