Dynamic microarchitectural adaptation using machine learning

Abstract

Adaptive microarchitectures are a promising solution for designing high-performance, power-efficient microprocessors. They offer the ability to tailor computational resources to the specific requirements of different programs or program phases. They have the potential to adapt the hardware cost-effectively at runtime to any application's needs. However, one of the key challenges is how to dynamically determine the best architecture configuration at any given time, for any new workload. This article proposes a novel control mechanism based on a predictive model for microarchitectural adaptivity control. This model is able to efficiently control adaptivity by monitoring the behaviour of an application's different phases at runtime. We show that by using this model on SPEC 2000, we double the energy/performance efficiency of the processor when compared to the best static configuration tuned for the whole benchmark suite. This represents 74% of the improvement available if we know the best microarchitecture for each program phase ahead of time. In addition, we present an extended analysis of the best configurations found and show that the overheads associated with the implementation of our scheme have a negligible impact on performance and power. © 2013 ACM.