On-chip EOL Prognostics Using Data-Fusion of Embedded Instruments for Dependable MP-SoCs

Ghazanfar Ali, Leila Bagheriye, Hans Manhaeve, Hans Gerard Kerkhoff

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Abstract

The usage of embedded instruments (EIs) in a processor core to address dependability challenges of modern-day Multi-Processor System-on-Chip (MP-SoC) has been studied in literature extensively. Data from these EIs can be used in applications like end-of-lifetime (EOL) predictions. However, inaccuracies present in the data from these EIs, due to their selfaging and resolution limitations during digitization, can lead to an inaccurate EOL assessment. In this paper, it is presented that in the presence of such inaccuracies from EIs as well as correlation between EIs, principal component analysis (PCA) based datafusion approach for determining the EOL of selected critical paths provided overall better EOL predictions as compared to EOL predictions based on standalone EIs. Verification was performed with a commercial software-based EOL predictor tool ARULE running on a personal computer. Moreover, the presented results on the computational requirements for the presented data-fusion approach showed little overhead in terms of memory, execution time and energy requirements.
Original languageEnglish
Title of host publicationIEEE Asian Test Symposium, ATS 2020
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)978-1-7281-7467-9
ISBN (Print)978-1-7281-7468-6
DOIs
Publication statusPublished - 23 Nov 2020
Event29th IEEE Asian Test Symposium, ATS 2020 - Penang, Malaysia
Duration: 23 Nov 202026 Nov 2020
Conference number: 29

Publication series

NameIEEE Asian Test Symposium (ATS)
PublisherIEEE
Number29
Volume2020
ISSN (Print)1081-7735
ISSN (Electronic)2377-5386

Conference

Conference29th IEEE Asian Test Symposium, ATS 2020
Abbreviated titleATS
CountryMalaysia
CityPenang
Period23/11/2026/11/20

Keywords

  • Dependability
  • Embedded instruments
  • Data fusion
  • Life-time prognostics
  • Machine learning

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