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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 language | English |
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Title of host publication | Proceedings of the 7th IEEE/ACM International Conference on Hardware/Software Codesign and System Synthesis |
Place of Publication | New York, NY, USA |
Publisher | ACM |
Pages | 315-324 |
Number of pages | 10 |
DOIs | |
Publication status | Published - Oct 2009 |
Publication series
Name | CODES+ISSS '09 |
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Publisher | ACM |
Keywords / Materials (for Non-textual outputs)
- continuous statistical machine learning, instruction set simulator, performance prediction
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Dive into the research topics of 'Using Continuous Statistical Machine Learning to Enable High-speed Performance Prediction in Hybrid Instruction-/Cycle-accurate Instruction Set Simulators'. Together they form a unique fingerprint.Projects
- 1 Finished
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PASTA: Automated systhesis of high performance low power embedded systems.
Topham, N., Franke, B. & O'Boyle, M.
1/03/06 → 5/09/10
Project: Research