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 the...
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| Published in: | Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design pp. 223 - 228 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
Washington, DC, USA
IEEE Computer Society
01.12.1995
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| Series: | ACM Conferences |
| Subjects: | |
| ISBN: | 9780818672132, 0818672137 |
| Online Access: | Get full text |
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| 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 in terms of cut size. |
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| AbstractList | 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 in terms of cut size. |
| Author | Cheng, Chung-Kuan Li, Jianmin Lillis, John |
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| Copyright | Copyright (c) 1995 Institute of Electrical and Electronics Engineers, Inc. All rights reserved. |
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| DOI | 10.5555/224841.224884 |
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| Keywords | max-flow min-cut linear placement circuit partitioning eigenvector |
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| Snippet | We propose a unified solution to both linear placement and partitioning. Our approach combines the well-known eigenvector optimization method with the... |
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| StartPage | 223 |
| SubjectTerms | Computing methodologies -- Symbolic and algebraic manipulation -- Symbolic and algebraic algorithms -- Linear algebra algorithms Hardware -- Electronic design automation -- Physical design (EDA) -- Placement Hardware -- Electronic design automation -- Physical design (EDA) -- Wire routing Mathematics of computing -- Mathematical analysis -- Mathematical optimization Mathematics of computing -- Mathematical analysis -- Numerical analysis -- Computations on matrices Theory of computation -- Design and analysis of algorithms -- Mathematical optimization |
| Title | Linear decomposition algorithm for VLSI design applications |
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