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Implicit Permutation Enumeration Networks and Binary Decision Diagrams Reordering

机译:隐式排列枚举网络和二元决策图重新排序

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Ordered Binary Decision Diagrams are a canonical representation of Boolean functions that is at the core of most formal verification systems and silicon compilers. Canonicity enables efficiency of manipulation but comes at the cost of fixing a variable evaluation order and predictably, BDD sizes are very sensitive to the selected order. The state-of-the-art reordering algorithms are based on transpositions of consecutive variables (swaps). Exact algorithms enumerate all possible n! permutations by performing a series of at least n! − 1 swaps, while heuristics typically either search for the optimum location of each variable independently (sifting) or utilize exact algorithms as a subroutine (k-window.) In this work we reduce the problem of variable ordering to that of obtaining a permutation enumeration transposition network. Our proposed network avoids traversing all n! permutations by exploiting structural properties of BDDs and requires the execution of fewer than 4n swaps sequentially instead of n! − 1. For the practically interesting cases of n = 4; 5 our algorithm requires only 11 (resp 59) sequential swaps instead of 23 (resp 119). We also propose an algorithm for moving between arbitrary variable orderings that executes at most n swaps sequentially, an improvement upon equation. Results suggest speedups of 162%; 308%;> 10X over the k - window heuristic for k = 4; 6; 8 and near-linear speedups when moving between orderings.
机译:有序二进制决策图是布尔函数的规范表示形式,它是大多数形式验证系统和芯片编译器的核心。正则性可以提高操作效率,但要以固定可变评估顺序为代价,并且可以预测,BDD大小对所选顺序非常敏感。最新的重新排序算法基于连续变量(掉期)的转置。精确的算法会列举所有可能的n!通过执行一系列至少n个排列! − 1交换,而启发式方法通常要么独立搜索(变量)每个变量的最佳位置,要么将精确算法用作子例程(k窗口)。在这项工作中,我们将变量排序的问题减少到获得置换枚举的问题。换位网络。我们建议的网络避免遍历所有n!通过利用BDD的结构属性进行排列,并且需要顺序执行少于4 n 个交换,而不是n! − 1.对于n = 4的实际情况; 5我们的算法只需要11次(resp 59)顺序交换,而不是23次(resp 119)。我们还提出了一种在任意变量顺序之间移动的算法,该算法最多依次执行n次交换,这是对等式的改进。结果表明速度提高了162%; 308%;> k = 4时在k-窗口启发式上的10倍; 6; 8和在订购之间移动时接近线性的加速。

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