Utilizing timing error detection and recovery to dynamically improve superscalar processor performance

Abstract

To provide reliable execution, traditional design methodologies perform timing error avoidance. Worst case parameters are assumed when determining a processor's operating frequency, allowing the maximum propagation delay through the system to be met. However, in practice the worst cases are rare, leading to a large amount of exploitable performance improvement if timing errors can be detected and recovered from. To this end, we propose a novel low cost scheme which allows a superscalar processor to dynamically tune its frequency past the worst case limit. When timing errors occur, they are detected and recovered from locally. Additionally, the number of errors that occur are monitored by one of several sampling methods. When the error rate becomes too high, leading to decreased performance, the frequency is scaled back. Experimental results show an average performance gain of 45% across all benchmark applications. The cost of implementing the error detection and recovery is kept modest by reusing the existing pipeline logic to detect the timing errors.