{"title":"Statistic Analysis of Power/Ground Networks Using Single-Node SOR Method","authors":"Zuying Luo, S. Tan","doi":"10.1109/ISQED.2008.62","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an efficient statistical analysis method for analyzing on-chip power grids. The new method, called SN-SOR (and its faster version, PSN- SOR), is based on a novel localized relaxed iterative approach and it can perform variational analysis on one node at a time. PSN-SOR further speeds up the analysis by using a refined conditioner, where the initial solution of SN-SOR is used as the pre-conditioner for the later iterations. Experimental results show that PSN-SOR is about two orders of magnitude(186X) faster than Monte- Carlo method with slight errors less than 5.685% on maximum and is about one order magnitude (41X) faster than general global successive over relaxation (SOR) method. PSN-SOR is more accurate and efficient than the recently proposed random walk method for localized statistical analysis.","PeriodicalId":243121,"journal":{"name":"9th International Symposium on Quality Electronic Design (isqed 2008)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"9th International Symposium on Quality Electronic Design (isqed 2008)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISQED.2008.62","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
In this paper, we propose an efficient statistical analysis method for analyzing on-chip power grids. The new method, called SN-SOR (and its faster version, PSN- SOR), is based on a novel localized relaxed iterative approach and it can perform variational analysis on one node at a time. PSN-SOR further speeds up the analysis by using a refined conditioner, where the initial solution of SN-SOR is used as the pre-conditioner for the later iterations. Experimental results show that PSN-SOR is about two orders of magnitude(186X) faster than Monte- Carlo method with slight errors less than 5.685% on maximum and is about one order magnitude (41X) faster than general global successive over relaxation (SOR) method. PSN-SOR is more accurate and efficient than the recently proposed random walk method for localized statistical analysis.
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