Using Continuous Statistical Machine Learning to Enable High-speed Performance Prediction in Hybrid Instruction-/Cycle-accurate Instruction Set Simulators

Daniel Christopher Powell, Björn Franke

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 7th IEEE/ACM International Conference on Hardware/Software Codesign and System Synthesis
Place of PublicationNew York, NY, USA
PublisherACM
Pages315-324
Number of pages10
DOIs
Publication statusPublished - Oct 2009

Publication series

NameCODES+ISSS '09
PublisherACM

Keywords / Materials (for Non-textual outputs)

  • continuous statistical machine learning, instruction set simulator, performance prediction

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