Life-Time Prognostics of Dependable VLSI-SoCs using Machine-learning

Leila Bagheriye, Ghazanfar Ali, Hans Gerard Kerkhoff

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

Recently, the usage of on-chip embedded instruments (EIs) to ensure dependable safety-critical systems is becoming inevitable. These EIs can help to provide self-awareness, and their feedback can be used in different applications, e.g. end-of-lifetime (EOL) predictions. However, inaccuracies present in data from these EIs, due to their resolution limitations, self-aging and quantization errors during digitization, can lead to an inaccurate EOL assessment. To address this challenge, a machine learning-based system-level approach for determining the EOL of a many-processor system-on-chip (MPSoC) is discussed. It is based on the synchronous data capture of different IJTAG compatible EIs. To this end, two different data fusion techniques have been used for enhancing the accuracy of lifetime prognostics of multiple EIs; use is made of Independent Component Analysis (ICA) and the auto-encoder (AE). Different combinations of fused EIs (based on ICA and AE) along with standalone EIs for four different critical paths (CPs) have been investigated. For lifetime prediction based on different EIs/fused EIs, a data-driven degradation model was derived, and nonlinear regression has been employed for parameter estimation. Results show that data fusion of different EIs helps in obtaining better estimation of the EOL as compared to using a standalone EI.
Original languageEnglish
Title of host publicationIEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-4
Number of pages4
ISBN (Electronic)978-1-7281-8187-5, 978-1-7281-8186-8
ISBN (Print)978-1-7281-8188-2
DOIs
Publication statusPublished - 13 Jul 2020
Event26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020 - Virtual, Online, Italy
Duration: 13 Jul 202016 Jul 2020
Conference number: 26
https://www.iolts2020virtual.cloud/welcome/

Publication series

NameIEEE International Symposium on On-Line Testing and Robust System Design (IOLTS)
PublisherIEEE
Number26
Volume2020
ISSN (Print)1942-9398
ISSN (Electronic)1942-9401

Conference

Conference26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020
Abbreviated titleIOLTS 2020
Country/TerritoryItaly
CityVirtual, Online
Period13/07/2016/07/20
Internet address

Keywords

  • Aging
  • Embedded instruments
  • Machine learning
  • Data fusion

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