{"title":"On-die power grids: The missing link","authors":"E. Chiprout","doi":"10.1145/1837274.1837511","DOIUrl":null,"url":null,"abstract":"Power grids with die-scale dimensions operate in a transient manner that is difficult to predict compared to larger power grids. Given a single excitation and a detailed model one can come to understand the dynamic effects occurring inside the die in terms of localized voltage droop scenarios. However, a major portion of understanding on-die power grids has to do with modeling the current stimulus pre-silicon for design purposes as well as generating a set of activities (via instructions) post-silicon in order to excite the worst case voltage droop. Any chip, especially a microprocessor, contains so many potential state transitions that it is not possible to simulate or enumerate all of them. A spectral-based learning and optimization method can alleviate this problem pre-silicon, while a micro-architectural based test generation scheme can help alleviate the problem post silicon.","PeriodicalId":87346,"journal":{"name":"Proceedings. Design Automation Conference","volume":"14 1","pages":"940-945"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. Design Automation Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1837274.1837511","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
Power grids with die-scale dimensions operate in a transient manner that is difficult to predict compared to larger power grids. Given a single excitation and a detailed model one can come to understand the dynamic effects occurring inside the die in terms of localized voltage droop scenarios. However, a major portion of understanding on-die power grids has to do with modeling the current stimulus pre-silicon for design purposes as well as generating a set of activities (via instructions) post-silicon in order to excite the worst case voltage droop. Any chip, especially a microprocessor, contains so many potential state transitions that it is not possible to simulate or enumerate all of them. A spectral-based learning and optimization method can alleviate this problem pre-silicon, while a micro-architectural based test generation scheme can help alleviate the problem post silicon.