The adoption of aggressively down-scaled voltages along with worsening process variations render nanometer devices prone to timing errors that threaten system functionality and output quality. In this paper, we introduce a significance-aware code vulnerability factor (SCVF) for early evaluation of the impact of such errors on applications. To estimate this metric, we propose the utilization of a microarchitecture-aware ML model for timing error prediction that jointly considers many instruction types, as well as all in-flight instructions in a pipeline. Finally, we validate the efficacy of SCVF by analyzing the output quality loss of various applications.
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