Accurate Estimation of Program Error Rate for Timing-Speculative Processors

We propose a framework that estimates the error rate experienced by an application as it runs on a timing-speculative processor. The framework uses an instruction error model that is comparable in accuracy to low-level simulations-as it considers the effects of operand values, preceding instructions, datapath configuration, and error correction scheme, as well as process variation, including its spatial correlation property-and yet efficient enough to allow its application in Monte Carlo experiments to characterize large program input datasets. We then use statistical limit theorems to estimate program error rate and quantify the effect of inter-instruction correlations.