{"title":"基于多电平特征值的电路划分技术","authors":"B. Schiffner, Jianhua Li, L. Behjat","doi":"10.1109/IWSOC.2005.17","DOIUrl":null,"url":null,"abstract":"VLSI circuit partitioning is an important step in the physical design of integrated circuits. In VLSI partitioning, a circuit is partitioned into smaller relatively independent sub-circuits. In this paper we present an eigenvalue based multilevel partitioning algorithm. The proposed method uses a matrix reordering technique to produce a minimal bandwidth matrix, relying upon matrix sparsity. The reordering technique is applied to the connectivity matrix of a clustered circuit and the matrix connectivity information is obtained. This connectivity information is used to partition the circuit. The experimental results demonstrate the technique's effectiveness against flat partitioning algorithms.","PeriodicalId":328550,"journal":{"name":"Fifth International Workshop on System-on-Chip for Real-Time Applications (IWSOC'05)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A multilevel eigenvalue based circuit partitioning technique\",\"authors\":\"B. Schiffner, Jianhua Li, L. Behjat\",\"doi\":\"10.1109/IWSOC.2005.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"VLSI circuit partitioning is an important step in the physical design of integrated circuits. In VLSI partitioning, a circuit is partitioned into smaller relatively independent sub-circuits. In this paper we present an eigenvalue based multilevel partitioning algorithm. The proposed method uses a matrix reordering technique to produce a minimal bandwidth matrix, relying upon matrix sparsity. The reordering technique is applied to the connectivity matrix of a clustered circuit and the matrix connectivity information is obtained. This connectivity information is used to partition the circuit. The experimental results demonstrate the technique's effectiveness against flat partitioning algorithms.\",\"PeriodicalId\":328550,\"journal\":{\"name\":\"Fifth International Workshop on System-on-Chip for Real-Time Applications (IWSOC'05)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fifth International Workshop on System-on-Chip for Real-Time Applications (IWSOC'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWSOC.2005.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fifth International Workshop on System-on-Chip for Real-Time Applications (IWSOC'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWSOC.2005.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multilevel eigenvalue based circuit partitioning technique
VLSI circuit partitioning is an important step in the physical design of integrated circuits. In VLSI partitioning, a circuit is partitioned into smaller relatively independent sub-circuits. In this paper we present an eigenvalue based multilevel partitioning algorithm. The proposed method uses a matrix reordering technique to produce a minimal bandwidth matrix, relying upon matrix sparsity. The reordering technique is applied to the connectivity matrix of a clustered circuit and the matrix connectivity information is obtained. This connectivity information is used to partition the circuit. The experimental results demonstrate the technique's effectiveness against flat partitioning algorithms.