基于进化算法和二元决策图的函数分解中输入变量划分方法

P. Morawiecki, M. Rawski
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引用次数: 4

摘要

功能分解是一种非常有效的数字电路和系统综合方法。然而,由于缺乏一种有效的输入变量划分方法,它对非常复杂的系统的实用性受到限制。针对以BDD表示的布尔函数的分解,提出了一种启发式的输入变量划分方法。该方法基于进化算法的应用,它允许探索问题的可能解空间,同时在这个简化的空间中保持高质量的解。布尔函数用简化有序二进制决策图(ROBDD)表示。实验结果表明,即使对于大型系统,所提出的启发式方法也能非常有效地生成最优或接近最优解。它比系统方法快得多,同时提供的结果质量相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Method of input variable partitioning in functional decomposition based on evolutionary algorithm and binary decision diagrams
The functional decomposition is recognized as very efficient synthesis method of digital circuits and systems. However its practical usefulness for very complex systems is limited by lack of an efficient method of input variable partitioning. In this paper, a heuristic method for input variable partitioning is proposed for decomposition of Boolean function represented by BDD. The method is based on an application of evolutionary algorithms, what allows exploring the possible solution space of a problem while keeping the high-quality solutions in this reduced space. The boolean function is represented by the reduced ordered binary decision diagram (ROBDD). The experimental results show that the proposed heuristic method is able to generate optimal or near optimal solution very efficiently even for large systems. It is much faster than the systematic method while delivering results of the comparable quality.
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