Accuracy analysis of machine learning-based performance modeling for microprocessors

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

This paper analyzes accuracy of performance models generated by machine learning-based empirical modeling methodology. Although the accuracy strongly depends on the quality of learning procedure, it is not clear what kind of learning algorithms and training data set (or feature) should be used. This paper inclusively explores the learning space of processor performance modeling as a case study. We focus on static architectural parameters as training data set such as cache size and clock frequency. Experimental results show that a tree-based non-linear regression modeling is superior to a stepwise linear regression modeling. Another observation is that clock frequency is the most important feature to improve prediction accuracy.

本文言語英語
ホスト出版物のタイトルProceedings of the 2016 4th International Japan-Egypt Conference on Electronic, Communication and Computers, JEC-ECC 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ページ83-86
ページ数4
ISBN(電子版)9781467389365
DOI
出版ステータス出版済み - 7 21 2016
イベント4th International Japan-Egypt Conference on Electronic, Communication and Computers, JEC-ECC 2016 - Cairo, エジプト
継続期間: 5 31 20166 2 2016

出版物シリーズ

名前Proceedings of the 2016 4th International Japan-Egypt Conference on Electronic, Communication and Computers, JEC-ECC 2016

その他

その他4th International Japan-Egypt Conference on Electronic, Communication and Computers, JEC-ECC 2016
国/地域エジプト
CityCairo
Period5/31/166/2/16

All Science Journal Classification (ASJC) codes

  • コンピュータ ネットワークおよび通信
  • 信号処理
  • コンピュータ サイエンスの応用

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