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 language | English |
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Title of host publication | IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020 |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Pages | 1-4 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-7281-8187-5, 978-1-7281-8186-8 |
ISBN (Print) | 978-1-7281-8188-2 |
DOIs | |
Publication status | Published - 13 Jul 2020 |
Event | 26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020 - Virtual, Online, Italy Duration: 13 Jul 2020 → 16 Jul 2020 Conference number: 26 https://www.iolts2020virtual.cloud/welcome/ |
Publication series
Name | IEEE International Symposium on On-Line Testing and Robust System Design (IOLTS) |
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Publisher | IEEE |
Number | 26 |
Volume | 2020 |
ISSN (Print) | 1942-9398 |
ISSN (Electronic) | 1942-9401 |
Conference
Conference | 26th IEEE International Symposium on On-Line Testing and Robust System Design, IOLTS 2020 |
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Abbreviated title | IOLTS 2020 |
Country/Territory | Italy |
City | Virtual, Online |
Period | 13/07/20 → 16/07/20 |
Internet address |
Keywords
- Aging
- Embedded instruments
- Machine learning
- Data fusion
- n/a OA procedure