On-chip EOL prognostics using data-fusion of embedded instruments for dependable MP-SoCs
Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE)
InProceedings of the 2020 IEEE 29th Asian Test Symposium (ATS), pp. 1-6
2020 IEEE 29th Asian Test Symposium (ATS) (Penang, Malaysia, 23-26 Nov. 2020)
Article in monograph or in proceedings
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SW OZ DCC AI
Proceedings of the 2020 IEEE 29th Asian Test Symposium (ATS)
SubjectCognitive artificial intelligence
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.
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