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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Veröffentlicht in:Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design S. 223 - 228
Hauptverfasser: Li, Jianmin, Lillis, John, Cheng, Chung-Kuan
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 01.12.1995
Schriftenreihe:ACM Conferences
Schlagworte:
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
max-flow min-cut
linear placement
circuit partitioning
eigenvector
ISBN:9780818672132, 0818672137
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
ISBN:9780818672132
0818672137
DOI:10.5555/224841.224884