Using Machine Learning to Guide Architecture Simulation
Abstract
An essential step in designing a new computer architecture is the careful examination of different design options. It is critical that computer architects have efficient means by which they may estimate the impact of various design options on the overall machine. This task is complicated by the fact that different programs, and even different parts of the same program, may have distinct behaviors that interact with the hardware in different ways. Researchers use very detailed simulators to estimate processor performance, which models every cycle of an executing program. Unfortunately, simulating every cycle of a real program can take weeks or months.
Cite
Text
Hamerly et al. "Using Machine Learning to Guide Architecture Simulation." Journal of Machine Learning Research, 2006.Markdown
[Hamerly et al. "Using Machine Learning to Guide Architecture Simulation." Journal of Machine Learning Research, 2006.](https://mlanthology.org/jmlr/2006/hamerly2006jmlr-using/)BibTeX
@article{hamerly2006jmlr-using,
title = {{Using Machine Learning to Guide Architecture Simulation}},
author = {Hamerly, Greg and Perelman, Erez and Lau, Jeremy and Calder, Brad and Sherwood, Timothy},
journal = {Journal of Machine Learning Research},
year = {2006},
pages = {343-378},
volume = {7},
url = {https://mlanthology.org/jmlr/2006/hamerly2006jmlr-using/}
}