On-Chip Embedded Instruments Data Fusion and Life-Time Prognostics of Dependable VLSI-SoCs using Machine-learning

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    Abstract

    Nowadays, a rapid introduction of very complex nanometer Many-Processor Systems-on-Chip in safety-critical applications is taking place. Unfortunately, it pairs with an unacceptable decrease in dependability of these complex nanosystems if no additional countermeasures are taken. To address this challenge, a promising approach is presented in this paper that uses a set of IJTAG compatible embedded instruments (EIs), in and around a processor cores to monitor their present health status. Data from these EIs is collected and fused for lifetime prognostics and hence dependability. For the EIs data fusion, use is made of principal component analysis (PCA) technique. For lifetime prediction based on different EIs, power-law degradation model was used.
    Original languageEnglish
    Title of host publicationIEEE International Symposium on Circuits and Systems, ISCAS 2020
    Place of PublicationPiscataway, NJ
    PublisherIEEE
    Pages1-5
    Number of pages5
    ISBN (Electronic)978-1-7281-3320-1
    ISBN (Print)978-1-7281-3321-8
    DOIs
    Publication statusPublished - 28 Sep 2020
    EventIEEE International Symposium on Circuits and Systems, ISCAS 2020 - Virtual Conference, Sevilla, Spain
    Duration: 10 Oct 202021 Oct 2020
    https://www.iscas2020.org/

    Publication series

    NameIEEE International Symposium on Circuits and Systems (ISCAS)
    PublisherIEEE
    Volume2020
    ISSN (Print)0271-4302
    ISSN (Electronic)2158-1525

    Conference

    ConferenceIEEE International Symposium on Circuits and Systems, ISCAS 2020
    Abbreviated titleISCAS
    CountrySpain
    CitySevilla
    Period10/10/2021/10/20
    Internet address

    Keywords

    • Dependability
    • Embedded instruments
    • Data fusion
    • Machine learning

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