Linear decomposition algorithm for VLSI design applications

Jianmin Li, J. Lillis, Chung-Kuan Cheng
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引用次数: 35

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.
线性分解算法在VLSI设计中的应用
我们提出了一个统一的线性布局和分区的解决方案。我们的方法结合了著名的特征向量优化方法和递归最大流最小切方法。提出了一种线性化特征向量法来改善线性布局。然后采用超图maxflow算法有效地找到最大流量最小割。在我们的统一方法中,最大流量最小切割提供了给定种子的最优有序划分,特征向量的放置为种子选择提供了启发式信息。在MCNC基准测试上的实验结果表明,我们的方法在线性放置和划分问题上都优于其他方法。平均而言,我们的方法在总导线长度方面比特征向量方法提高45.1%,在切割尺寸方面比抛物线方法提高26.9%[6]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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