一种基于概率的VLSI电路划分方法

S. Dutt, W. Deng
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引用次数: 87

摘要

迭代改进的双向最小切割划分是大多数电路划分工具的一个重要阶段。大多数电路网络表的迭代改进技术,如Fidducia-Mattheyses (FM)方法,都是使用本地网络表信息计算节点的增益,而这些信息只与切割集中的即时改进有关。这可能导致误导性的增益计算。Krishnamurthy提出了一种前瞻性(LA)增益计算方法来改善这种情况;然而,正如我们所示,它留下了相当大的改进空间。我们在这里提出了一种称为PROP的概率增益计算方法,该方法能够捕获当前移动节点的全局和未来影响。实验结果表明,对于相同的运行次数,PROP的性能比FM(约30%)和LA(约27%)要好得多,并且也比许多最近最先进的基于聚类的分区器(如EIG1、WINDOW、MELO和抛物线)好15%到57%。我们也证明了PROP的时空复杂性是非常合理的。我们的经验计时结果表明,它比上述基于聚类的技术快得多,只比FM和LA慢一点,这两种技术都非常快。
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A probability-based approach to VLSI circuit partitioning
Iterative-improvement 2-way min-cut partitioning is an important phase in most circuit partitioning tools. Most iterative improvement techniques for circuit netlists like the Fidducia-Mattheyses (FM) method compute the gains of nodes using local netlist information that is only concerned with the immediate improvement in the cutset. This can lead to misleading gain calculations. Krishnamurthy suggested a lookahead (LA) gain calculation method to ameliorate this situation; however, as we show, it leaves considerable room for improvement. We present here a probabilistic gain computation approach called PROP that is capable of capturing the global and future implications of moving a node at the current time. Experimental results show that for the same number of runs, PROP performs much better than FM (by about 30%) and LA (by about 27%), and is also better than many recent state-of-the-art clustering-based partitioners like EIG1, WINDOW, MELO and PARABOLI by 15% to 57%. We also show that the space and time complexities of PROP are very reasonable. Our empirical timing results reveal that it is appreciably faster than the above clustering-based techniques, and only a little slower than FM and LA, both of which are very fast.
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