{"title":"基于赤池信息准则的低功耗应用最优概率模型选择","authors":"R. Chandramouli, V. Srikantam","doi":"10.1109/ISCAS.2000.857132","DOIUrl":null,"url":null,"abstract":"Optimal probability model selection for power estimation in low power VLSI applications is studied. Akaike's information criterion is used to estimate the optimal number of components in a mixture density model for the simulated power data. Theory behind the proposed algorithm is discussed followed by experimental results for ISCAS '85 benchmark circuits and a large industrial circuit. The method is shown to perform well for both large and small circuits even when the number of observed samples is small. The algorithm is promising as a pre-processing step to automatically compute the optimal probability model before any other power estimation procedure is applied. We also note that the method is applicable to other problems in VLSI for model selection.","PeriodicalId":6422,"journal":{"name":"2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2000-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Optimum probability model selection using Akaike's information criterion for low power applications\",\"authors\":\"R. Chandramouli, V. Srikantam\",\"doi\":\"10.1109/ISCAS.2000.857132\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Optimal probability model selection for power estimation in low power VLSI applications is studied. Akaike's information criterion is used to estimate the optimal number of components in a mixture density model for the simulated power data. Theory behind the proposed algorithm is discussed followed by experimental results for ISCAS '85 benchmark circuits and a large industrial circuit. The method is shown to perform well for both large and small circuits even when the number of observed samples is small. The algorithm is promising as a pre-processing step to automatically compute the optimal probability model before any other power estimation procedure is applied. We also note that the method is applicable to other problems in VLSI for model selection.\",\"PeriodicalId\":6422,\"journal\":{\"name\":\"2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCAS.2000.857132\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCAS.2000.857132","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimum probability model selection using Akaike's information criterion for low power applications
Optimal probability model selection for power estimation in low power VLSI applications is studied. Akaike's information criterion is used to estimate the optimal number of components in a mixture density model for the simulated power data. Theory behind the proposed algorithm is discussed followed by experimental results for ISCAS '85 benchmark circuits and a large industrial circuit. The method is shown to perform well for both large and small circuits even when the number of observed samples is small. The algorithm is promising as a pre-processing step to automatically compute the optimal probability model before any other power estimation procedure is applied. We also note that the method is applicable to other problems in VLSI for model selection.