Estimating Code Vulnerability to Timing Errors Via Microarchitecture-Aware Machine Learning
نویسندگان
چکیده
This article addresses the microarchitecture-aware modeling of timing errors and estimation vulnerability SW programs to such errors. A significance-aware code factor (SCVF) quantifies susceptibility applications errors, utilizing a machine learning (ML)-based error prediction model. is complemented by workloadaware model, which based on supervised ML methods.
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ژورنال
عنوان ژورنال: IEEE design & test
سال: 2023
ISSN: ['2168-2364', '2168-2356']
DOI: https://doi.org/10.1109/mdat.2021.3135318