{"title":"基于聚类表的RTL功率估计宏模型","authors":"Roberto Corgnati, E. Macii, M. Poncino","doi":"10.1109/GLSV.1999.757455","DOIUrl":null,"url":null,"abstract":"Macromodeling is considered the most effective approach to RTL power estimation. Among the macromodels presented in the literature, table-based ones have overcome some of the limitations of conventional, equation-based solutions. In this paper we propose some enhancements to the basic implementation of table-based macromodels that improve the estimation accuracy while preserving the intrinsic robustness.","PeriodicalId":127222,"journal":{"name":"Proceedings Ninth Great Lakes Symposium on VLSI","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Clustered table-based macromodels for RTL power estimation\",\"authors\":\"Roberto Corgnati, E. Macii, M. Poncino\",\"doi\":\"10.1109/GLSV.1999.757455\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Macromodeling is considered the most effective approach to RTL power estimation. Among the macromodels presented in the literature, table-based ones have overcome some of the limitations of conventional, equation-based solutions. In this paper we propose some enhancements to the basic implementation of table-based macromodels that improve the estimation accuracy while preserving the intrinsic robustness.\",\"PeriodicalId\":127222,\"journal\":{\"name\":\"Proceedings Ninth Great Lakes Symposium on VLSI\",\"volume\":\"89 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Ninth Great Lakes Symposium on VLSI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GLSV.1999.757455\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Ninth Great Lakes Symposium on VLSI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GLSV.1999.757455","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Clustered table-based macromodels for RTL power estimation
Macromodeling is considered the most effective approach to RTL power estimation. Among the macromodels presented in the literature, table-based ones have overcome some of the limitations of conventional, equation-based solutions. In this paper we propose some enhancements to the basic implementation of table-based macromodels that improve the estimation accuracy while preserving the intrinsic robustness.