Simple eigenvector-based circuit clustering can be effective [VLSI CAD]
Simple eigenvector-based circuit clustering can be effective [VLSI CAD]
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简单的基于特征向量的电路聚类可能是有效的 [VLSI CAD]
DOI：
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发表时间：
1996
期刊：
影响因子：
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通讯作者：
A. Kahng
中科院分区：
文献类型：
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作者：
C. Alpert;A. Kahng
事实证明，聚类有效地提高了VLSI NetList分区和放置算法的质量。已经提出了多种聚类方案，包括随机步行，迭代匹配和相当复杂的光谱技术。我们使用特征向量计算聚类，但以最简单，最明显的方式进行。我们的算法首先根据I/ SUP Th/ sup th/ sup th/ sub i/ sup/ sup th/ sup th/ sup th/ sup th/ sup th/ sup th/ sup th/ sup的算法代码。然后，将具有相同代码的模块分配给同一集群。尽管它很简单，但这种新的聚类算法还是由光谱两部分和多维矢量分配的理论结果强烈的动机。该算法还具有线性时间复杂性（不包括特征向量计算），并且至少在两阶段的贴纤维 - 摩托比亚 - 摩特学院两部分方面与以前的聚类算法一样有效。
Clustering has proven effective in improving the quality of VLSI netlist partitioning and placement algorithms. A wide variety of clustering schemes have been proposed, including random walks, iterative matching, and fairly complicated spectral techniques. We use eigenvectors to compute a clustering, but do so in the simplest, most obvious manner. Our algorithm first computes a d-digit code for each module v/sub i/ according to the signs of the i/sup th/ entries in a set of d eigenvectors. Then, modules with the same code are assigned to the same cluster. Despite its simplicity, this new clustering algorithm is strongly motivated by theoretical results for both spectral bipartitioning and multi-dimensional vector partitioning. The algorithm also has linear time complexity (not including the eigenvector computation) and is at least as effective as previous clustering algorithms in terms of two-phase Fiduccia-Mattheyses bipartitioning.