%0 Journal Article
%A PANG Jiufeng
%A LI Xianfeng
%A XIE Jinsong
%A TONG Dong
%A CHENG Xu
%T Microarchitectural Design Space Exploration via Support Vector Machine
%D 2010
%R
%J Acta Scientiarum Naturalium Universitatis Pekinensis
%P 55-63
%V 46
%N 1
%X The authors propose an approachto reducethe number of required si mulations, simulate on sampled design points, and use it to construct informative and predictive support vector regression models. Having captured the interacting effects of design parameters, the models predict outputs for design points that are not simulated. The prediction time of model can be negligible compared with detailed simulation. The optimal design point determined by prediction is very close to that of simulation for most applications and provides an efficient wayto cull huge design space. Trained on only 0.26 % design points, the models yield mean relative prediction error as low as 0 .52 % for performance and 1 .08 % for power. Correlation analysis demonstrates that prediction output is highly correlated with simulated observation. The average squared correlation coefficient is 0.728 for performance models while 0.703 for power models, which implies that support vector regressions capture most of relationships among design parameters. The model also provides a predictive probability interval for each prediction, which is informative for computer architects.
%U https://xbna.pku.edu.cn/EN/abstract/article_360.shtml