AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (7.2 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
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
Show Author Information

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

References

[1]
R. Piscitelli and A. D. Pimentel, Design space pruning through hybrid analysis in system-level design space exploration, in Proc. Design Automation and Test in Europe Conf. and Exhibition (DATE), Dresden, Germany, 2012, pp. 781–786.
Crossref Google Scholar
[2]
J. Sepulveda, G. Gogniat, R. Pires, W. J. Chau, and M. Strum, Hybrid-on-chip communication architecture for dynamic MP-SoC protection, in Proc. 25th Symp. Integrated Circuits and Systems Design (SBCCI), Brasilia, Brazil, 2012, pp. 1–6.
Crossref Google Scholar
[3]
P. Ghosh and K. Sinha, A framework for evaluation of debug path performance in SoC, in Proc. IEEE 34th Int. System-on-Chip Conf. (SOCC), Las Vegas, NV, USA, 2021, pp. 188–193.
Crossref Google Scholar
[4]
Y. Zhao and H. G. Kerkhoff, Highly dependable multi-processor SoCs employing lifetime prediction based on health monitors, in Proc. IEEE 25th Asian Test Symp. (ATS), Hiroshima, Japan, 2016, pp. 228–233.
Crossref Google Scholar
[5]
N. Parihar, R. G. Southwick, M. M. Wang, J. H. Stathis, and S. Mahapatra, Modeling of NBTI Kinetics in RMG Si and SiGe FinFETs, Part-I: DC stress and recovery, IEEE Trans. Electron Devices, vol. 65, no. 5, pp. 1699–1706, 2018.
Crossref Google Scholar
[6]
G. Moro, A. Bin, R. H. Jack, C. Heinrichs, and A. P. McPherson, Making high-performance embedded instruments with Bela and Pure Data, in Proc. Int. Conf. of Live Interfaces, Brighton, UK, 2016, pp. 1–6.
Google Scholar
[7]
H. M. Von Staudt, M. A. Benhebibi, J. Rearick, and M. Laisne, Industrial application of IJTAG Standards to the test of big-A/little-d devices, in Proc. IEEE Int. Test Conf. (ITC), Washington, DC, USA, 2020, pp. 1–10.
Crossref Google Scholar
[8]
Z. Zhou, Machine Learning. Singapore: Springer, 2021.
Crossref
[9]
G. Hesamian and M. G. Akbari, A robust varying coefficient approach to fuzzy multiple regression model, J. Comput. Appl. Math., vol. 371, p. 112704, 2020.
Crossref Google Scholar
[10]
W. Wang, A model to determine the optimal critical level and the monitoring intervals in condition-based maintenance, Int. J. Prod. Res., vol. 38, pp. 1425–1436, 2000.
Crossref Google Scholar
[11]
C. J. Lu and W. O. Meeker, Using degradation measures to estimate a time-to-failure distribution, Technometrics, vol. 35, no. 2, pp. 161–174, 1993.
Crossref Google Scholar
[12]
N. Z. Gebraeel, M. A. Lawley, R. Li, and J. K. Ryan, Residual-life distributions from component degradation signals: A Bayesian approach, IIE Trans., vol. 37, no. 6, pp. 543–557, 2005.
Crossref Google Scholar
[13]
H. Zhao, J. Zheng, J. Xu, and W. Deng, Fault diagnosis method based on principal component analysis and broad learning system, IEEE Access, vol. 7, pp. 99263–99272, 2019.
Crossref Google Scholar
[14]
S. Mirjalili, J. S. Dong, A. S. Sadiq, and H. Faris, Genetic algorithm: Theory, literature review, and application in image reconstruction, in Nature-Inspired Optimizers: Theories, Literature Reviews and Applications. Switzerland, Springer, 2020, pp. 69–85.
Crossref
[15]
T. D. Braak, Run-time mapping: Dynamic resource allocation in embedded systems, PhD dissertation, Twente University, Enschede, the Netherlands, 2016.
[16]
Y. Zhao and H. G. Kerkhoff, Application of functional IDDQ testing in a VLIW processor towards detection of aging degradation, in Proc. 10th Int. Conf. Design and Technology of Integrated Systems in Nanoscale Era (DTIS), Naples, Italy, 2015, pp. 1–5.
Google Scholar
[17]
JEDEC standard JESD22-A105C, https://www.jedec.org/standards-documents/docs/jesd-22-a105c, 2020.
[18]
A. Sonmez, E. Kocyigit, and E. Kugu, Optimal path planning for UAVs using genetic algorithm, in Proc. Int. Conf. Unmanned Aircraft Systems (ICUAS), Denver, CO, USA, 2015, pp. 50–55.
Crossref Google Scholar
[19]
C. Kwon and S. D. Sudhoff, Genetic algorithm-based induction machine characterization procedure with application to maximum torque per amp control, IEEE Trans. Energy Convers., vol. 21, no. 2, pp. 405–415, 2006.
Crossref Google Scholar
[20]
J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge, MA, USA: MIT Press, 1992.
Crossref
[21]
J. C. Spall, Introduction to Stochastic Search and Optimization. Hoboken, NJ, USA: John Wiley and Sons, Inc., 2003.
Crossref
[22]
R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, in Proc. 14th Int. Joint Conf. Artificial intelligence, Montreal, Canada, 1995, pp. 1137–1145.
Google Scholar
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

3686

Views

550

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics
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/).
Return