Estimating Code Vulnerability to Timing Errors Via Microarchitecture-Aware Machine Learning

IEEE design & test(2023)

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摘要
This article addresses the microarchitecture-aware modeling of timing errors and the estimation of the vulnerability of SW programs to such errors. A significance-aware code vulnerability factor (SCVF) quantifies the susceptibility of applications to such timing errors, utilizing a machine learning (ML)-based error prediction model. This is complemented by a workloadaware error prediction model, which is based on supervised ML methods.
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关键词
Timing,Measurement,Machine learning,Predictive models,History,Microarchitecture,Data models,Codes,Timing error modelling,machine-learning,microarchitecture,dynamic timing analysis,application error vulnerability
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