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OBDD minimization based on two-level representation of Boolean functions

机译:基于布尔函数的两级表示的OBDD最小化

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In this paper, we analyze the basic properties of some Boolean function classes and propose a low complexity OBDD variable ordering algorithm, which is exact (optimum) to some classes of functions and very effective to general two-level form functions. We show that the class of series-parallel functions, which can be expressed by a factored form where each variable appears exactly once, can yield exact OBDD variable orderings in polynomial time. We also study the thin Boolean functions whose corresponding OBDDs can be represented by the form of thin OBDDs in which the number of nonterminal nodes is equal to the number of input variables. We show,that a thin Boolean function always has an essential prime cube cover and the class of series-parallel functions is a proper subset of thin Boolean functions. We propose a heuristic viewing OBDDs as evaluation machines with function cube covers as their inputs and apply a queuing principle in the algorithm design. Our heuristic, the augmented Dynamic Shortest Cube First algorithm, is proven to be optimum for the series-parallel functions and also be very effective for general two-level form functions. Experimental results on a large number of two-level form benchmark circuits show that the algorithm yields an OBDD total size reduction of over 51 percent with only 7 percent CPU time compared to the well-known network-based Fan-in Heuristic implemented in the SIS package. Comparing to the known exact results, ours is only 49 percent larger in size while only uses 0.001 percent CPU time.
机译:在本文中,我们分析了一些布尔函数类的基本属性,并提出了一种低复杂度的OBDD变量排序算法,该算法对某些函数类是精确的(最佳),并且对于一般的两级形式函数非常有效。我们表明,可以通过阶乘形式(其中每个变量恰好出现一次)表示的一系列并行函数,可以在多项式时间内产生精确的OBDD变量排序。我们还研究了细布尔函数,其布尔值可以用细OBDD的形式表示,其中非终端节点的数量等于输入变量的数量。我们证明,薄布尔函数始终具有基本的素立方覆盖,而串并联函数的类别是薄布尔函数的适当子集。我们提出一种启发式的观察OBDD作为评估机,以功能多维数据集封面作为其输入,并在算法设计中应用排队原则。我们的启发式算法,即增强的动态最短多维数据集优先算法,被证明对串并函数是最佳的,对一般的两级形式函数也非常有效。在大量两级基准电路上的实验结果表明,与SIS中实现的基于网络的著名Fan-in启发式算法相比,该算法可将OBDD总大小减少51%以上,而CPU时间仅为7%。包。与已知的精确结果相比,我们的结果仅大49%,而仅占用0.001%的CPU时间。

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