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

作　　者：Yong Zhao Longkun Guo Xiaoyan Zhang 

机构地区：[1]NXP Semiconductor,Eindhoven 5656,the Netherlands [2]School of Math and Statistics,Fuzhou University,Fuzhou 350108,China [3]School of Mathematical Science and Institute of Mathematics,Nanjing Normal University,Nanjing 210023,China

出　　处：《Tsinghua Science and Technology》2023年第6期1041-1049,共9页清华大学学报（自然科学版（英文版）

基　　金：This study was supported by the National Natural Science Foundation of China(Nos.12271259,12271098,and 11971349);EU project BASTION(No.619871);Horizon 2020 IMMORTAL(No.644905);Recore Systems B.V.(the Netherlands);Ridgetop Group Inc.(the Netherlands)are acknowledged for their contributions to IC design and measurement。

摘　　要：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.

关 键 词：data fusion genetic algorithm lifetime prediction health monitor multi-core System-on-Chips(SoCs) embedded instruments 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]