Lazy group sifting for efficient symbolic state traversal of FSMs
Lazy group sifting for efficient symbolic state traversal of FSMs
复制标题
用于高效 FSM 符号状态遍历的惰性组筛选
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
F. Somenzi
中科院分区：
文献类型：
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作者：
H. Higuchi;F. Somenzi
针对有限状态机状态遍历过程中的动态变量重排序问题，提出了惰性组筛选方法。所提出的方法放松了对当前状态变量及其对应的下一状态变量进行成对分组的思想。这样做是为了在图像计算期间产生更好的变量排序，而不会在图像计算结束时用当前状态变量替换下一个状态变量时导致BDD（二元决策图）大小爆炸。实验结果表明，我们的方法是更强大的状态遍历比方法，无论是无条件分组变量对或从来没有分组。
Proposes lazy group sifting for dynamic variable reordering during state traversal of finite state machines (FSMs). The proposed method relaxes the idea of pairwise grouping of the present state variables and their corresponding next state variables. This is done to produce better variable orderings during image computation without causing BDD (binary decision diagram) size blowup in the substitution of next state variables with present state variables at the end of image computation. Experimental results show that our approach is more robust in state traversal than the approaches that either unconditionally group variable pairs or never group them.