Performance Analysis of Timing-Speculative Processors

Abstract

We propose a framework to estimate the number of timing errors experienced by an application as it runs on a timing-speculative processor. It takes a hybrid approach combining an accurate gate-level dynamic timing analysis engine to find timing errors in the processor's control network with a fast architecture-level execution-driven simulator based on a path activation model of the datapath. We develop an instruction-level error model that estimates the likelihood of an instruction experiencing a timing error, capturing the effects of process and data variations as well as inter-instruction correlations caused by the error recovery scheme used by the processor. Finally, we utilize two well-known laws of applied statistics, the law of small numbers and the law of large numbers, to estimate, with bounded inaccuracy, the total number of timing errors experienced by a specific application. Our experiments show that the combination of running application and its input data can change performance by as much as 25 percent, demonstrating that application-specific analysis is necessary for accurate evaluation of timing-speculative processors and should be used to inform design decisions and assess the suitability of applications for timing speculation.