{"title":"线性分解算法在VLSI设计中的应用","authors":"Jianmin Li, J. Lillis, Chung-Kuan Cheng","doi":"10.1109/ICCAD.1995.480016","DOIUrl":null,"url":null,"abstract":"We propose a unified solution to both linear placement and partitioning. Our approach combines the well-known eigenvector optimization method with the recursive max-flow min-cut method. A linearized eigenvector method is proposed to improve the linear placement. A hypergraph maxflow algorithm is then adopted to efficiently find the max-flow min-cut. In our unified approach, the max-flow min-cut provides an optimal ordered partition subject to the given seeds and the eigenvector placement provides heuristic information for seed selection. Experimental results on MCNC benchmarks show that our approach is superior to other methods for both linear placement and partitioning problems. On average, our approach yields an improvement of 45.1% over eigenvector approach in terms of total wire length, and yields an improvement of 26.9% over PARABOLI[6] in terms of cut size.","PeriodicalId":367501,"journal":{"name":"Proceedings of IEEE International Conference on Computer Aided Design (ICCAD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"35","resultStr":"{\"title\":\"Linear decomposition algorithm for VLSI design applications\",\"authors\":\"Jianmin Li, J. Lillis, Chung-Kuan Cheng\",\"doi\":\"10.1109/ICCAD.1995.480016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a unified solution to both linear placement and partitioning. Our approach combines the well-known eigenvector optimization method with the recursive max-flow min-cut method. A linearized eigenvector method is proposed to improve the linear placement. A hypergraph maxflow algorithm is then adopted to efficiently find the max-flow min-cut. In our unified approach, the max-flow min-cut provides an optimal ordered partition subject to the given seeds and the eigenvector placement provides heuristic information for seed selection. Experimental results on MCNC benchmarks show that our approach is superior to other methods for both linear placement and partitioning problems. On average, our approach yields an improvement of 45.1% over eigenvector approach in terms of total wire length, and yields an improvement of 26.9% over PARABOLI[6] in terms of cut size.\",\"PeriodicalId\":367501,\"journal\":{\"name\":\"Proceedings of IEEE International Conference on Computer Aided Design (ICCAD)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"35\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of IEEE International Conference on Computer Aided Design (ICCAD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAD.1995.480016\",\"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 of IEEE International Conference on Computer Aided Design (ICCAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAD.1995.480016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Linear decomposition algorithm for VLSI design applications
We propose a unified solution to both linear placement and partitioning. Our approach combines the well-known eigenvector optimization method with the recursive max-flow min-cut method. A linearized eigenvector method is proposed to improve the linear placement. A hypergraph maxflow algorithm is then adopted to efficiently find the max-flow min-cut. In our unified approach, the max-flow min-cut provides an optimal ordered partition subject to the given seeds and the eigenvector placement provides heuristic information for seed selection. Experimental results on MCNC benchmarks show that our approach is superior to other methods for both linear placement and partitioning problems. On average, our approach yields an improvement of 45.1% over eigenvector approach in terms of total wire length, and yields an improvement of 26.9% over PARABOLI[6] in terms of cut size.