Using continuous statistical machine learning to enable high-speed performance prediction in hybrid instruction-/cycle-accurate instruction set simulators

Abstract

Functional instruction set simulators perform instruction-accurate simulation of benchmarks at high instruction rates. Unlike their slower, but cycle-accurate counterparts however, they are not capable of providing cycle counts due to the higher level of hardware abstraction. In this paper we present a novel approach to performance prediction based on statistical machine learning utilizing a hybrid instruction- and cycle-accurate simulator. We introduce the concept of continuous machine learning to simulation whereby new training data points are acquired on demand and used for on-the-fly updates of the performance model. Furthermore, we show how statistical regression can be adapted to reduce the cost of these updates during a performance-critical simulation. For a state-of-the-art simulator modeling the ARC 750D embedded processor we demonstrate that our approach is highly accurate, with average error <2.5% while achieving a speed-up of approx. 50% over the baseline cycle-accurate simulation.