Neural clustering algorithms for classification and pre-placement of VLSI cells

L. Raffo, D. Caviglia, G. Bisio
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引用次数: 1

Abstract

The authors present modifications to Kohonen autoassociative maps to increase their efficiency for clustering and decrease their sensitivity to initial conditions. A new update rule is described for the classification for similarity. Some test results are presented for comparison between different algorithms. The new neural network algorithm was applied to the problem of preplacement of VLSI cells with improvement in the quality of the solution and computational time.<>
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VLSI细胞分类与预放置的神经聚类算法
作者对Kohonen自关联映射进行了改进,提高了它们的聚类效率,降低了它们对初始条件的敏感性。为相似性分类描述了一个新的更新规则。给出了一些测试结果,对不同算法进行了比较。将新的神经网络算法应用于超大规模集成电路单元置换问题,提高了求解质量和计算时间。
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Neural clustering algorithms for classification and pre-placement of VLSI cells General-to-specific learning of Horn clauses from positive examples Minimization of NAND circuits by rewriting-rules heuristic A generalized stochastic Petri net model of Multibus II Activation of connections to accelerate the learning in recurrent back-propagation
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