{"title":"用机器学习模型预测处理器性能","authors":"A. Beg","doi":"10.1109/MWSCAS.2007.4488749","DOIUrl":null,"url":null,"abstract":"Architectural simulators are traditionally used to study the design trade-offs for processor systems. The simulators are implemented in a high-level programming language or a hardware descriptive language, and are used to estimate the system performance prior to the hardware implementation. The simulations, however, may need to run for long periods of time for even a small set of design variations. In this paper, we propose a machine learnt (neural network/NN) model for estimating the execution performance of a superscalar processor. Multiple runs for the model are finished in less than a few milliseconds as compared to days or weeks required for simulation-based methods. The model is able to predict the execution throughput of a processor system with over 85% accuracy when tested with six SPEC2000 CPU integer benchmarks. The proposed model has possible applications in computer architecture research and teaching.","PeriodicalId":256061,"journal":{"name":"2007 50th Midwest Symposium on Circuits and Systems","volume":"3 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Predicting processor performance with a machine learnt model\",\"authors\":\"A. Beg\",\"doi\":\"10.1109/MWSCAS.2007.4488749\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Architectural simulators are traditionally used to study the design trade-offs for processor systems. The simulators are implemented in a high-level programming language or a hardware descriptive language, and are used to estimate the system performance prior to the hardware implementation. The simulations, however, may need to run for long periods of time for even a small set of design variations. In this paper, we propose a machine learnt (neural network/NN) model for estimating the execution performance of a superscalar processor. Multiple runs for the model are finished in less than a few milliseconds as compared to days or weeks required for simulation-based methods. The model is able to predict the execution throughput of a processor system with over 85% accuracy when tested with six SPEC2000 CPU integer benchmarks. The proposed model has possible applications in computer architecture research and teaching.\",\"PeriodicalId\":256061,\"journal\":{\"name\":\"2007 50th Midwest Symposium on Circuits and Systems\",\"volume\":\"3 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 50th Midwest Symposium on Circuits and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MWSCAS.2007.4488749\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 50th Midwest Symposium on Circuits and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MWSCAS.2007.4488749","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting processor performance with a machine learnt model
Architectural simulators are traditionally used to study the design trade-offs for processor systems. The simulators are implemented in a high-level programming language or a hardware descriptive language, and are used to estimate the system performance prior to the hardware implementation. The simulations, however, may need to run for long periods of time for even a small set of design variations. In this paper, we propose a machine learnt (neural network/NN) model for estimating the execution performance of a superscalar processor. Multiple runs for the model are finished in less than a few milliseconds as compared to days or weeks required for simulation-based methods. The model is able to predict the execution throughput of a processor system with over 85% accuracy when tested with six SPEC2000 CPU integer benchmarks. The proposed model has possible applications in computer architecture research and teaching.