On-the-fly and DAG-aware: Rewriting Boolean Networks with Exact Synthesis

Heinz Riener, Winston Haaswijk, A. Mishchenko, G. Micheli, Mathias Soeken
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引用次数: 34

Abstract

The paper presents a generalization of DAG-aware AIG rewriting for k-feasible Boolean networks, whose nodes are k-input lookup tables (k-LUTs). We introduce a high-effort DAG-aware rewriting algorithm, called cut rewriting, which uses exact synthesis to compute replacements on the fly, with support for Boolean don’t cares. Cut rewriting pre-computes a large number of possible replacement candidates, but instead of eagerly rewriting the Boolean network, stores the replacements in a conflict graph. Heuristic optimization is used to derive a best, maximal subset of replacements that can be simultaneously applied to the Boolean network from the conflict graph. We optimize LUT mapped Boolean networks obtained from the ISCAS and EPFL combinational benchmark suites. For 3-LUT networks, experiments show that we achieve an average size improvement of 5.58% and up to 40.19% after state-of-the-art Boolean rewriting techniques were applied until saturation. Similarly, for 4-LUT networks, we obtain an average improvement of 4.04% and up to 12.60%.
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动态和dag感知:用精确合成重写布尔网络
针对节点为k输入查找表的k可行布尔网络,提出了一种基于dag感知的AIG改写方法。我们引入了一种高效的dag感知重写算法,称为切割重写,它使用精确的合成来动态地计算替换,并支持布尔不在乎。Cut重写预先计算了大量可能的替代候选,但不是急切地重写布尔网络,而是将替换存储在冲突图中。启发式优化用于从冲突图中导出可同时应用于布尔网络的最佳、最大替换子集。我们优化了从ISCAS和EPFL组合基准套件中获得的LUT映射布尔网络。对于3-LUT网络,实验表明,在应用最先进的布尔重写技术直到饱和之后,我们实现了5.58%的平均大小改进,最高可达40.19%。同样,对于4-LUT网络,我们获得了4.04%到12.60%的平均改进。
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