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Open Access

Data Fusion with Genetic Algorithm Based Lifetime Prediction for Dependable Multi-Processor System-on-Chips

NXP Semiconductor, Eindhoven 5656, the Netherlands
School of Math and Statistics, Fuzhou University, Fuzhou 350108, China
School of Mathematical Science and Institute of Mathematics, Nanjing Normal University, Nanjing 210023, China
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Abstract

With the prevalence of big-data technology, intricate, nanoscale Multi-Processor System-on-Chips (MP-SoCs) have been used in various safety-critical applications. However, with no extra countermeasures taken, this widespread use of MP-SoCs can lead to an undesirable decrease in their dependability. This study presents a promising approach using a group of Embedded Instruments (EIs) inside a processor core for health monitoring. Multiple health monitoring datasets obtained from the employed EIs are sampled and collated via the implemented experiment and thereafter used for conducting its remaining useful lifetime prognostics. This enables MP-SoCs to undertake preventive self-repair, thus realizing a zero mean downtime system and ensuring improved dependability. In addition, a principal component analysis based algorithm is designed for realizing the EI data fusion. Subsequently, a genetic algorithm based degradation optimization is employed to create a lifetime prediction model with respect to the processor.

Keywords

data fusiongenetic algorithmlifetime predictionhealth monitormulti-core System-on-Chips (SoCs)embedded instruments

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Tsinghua Science and Technology
Volume 28 Issue 6,
December 2023
Pages 1041-1049
DOI: 10.26599/TST.2022.9010053
Cite this article:
Zhao Y, Guo L, Zhang X. Data Fusion with Genetic Algorithm Based Lifetime Prediction for Dependable Multi-Processor System-on-Chips. Tsinghua Science and Technology, 2023, 28(6): 1041-1049. https://doi.org/10.26599/TST.2022.9010053

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Received: 08 August 2022
Revised: 28 September 2022
Accepted: 08 November 2022
Published: 28 July 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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