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arXiv 2403.05928cs.LO
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结构感知计算、部分量化消除与SAT

Structure-Aware Computing, Partial Quantifier Elimination And SAT

Eugene Goldberg

AI总结:

本文探讨了通过部分量化消除实现结构感知计算的方法,展示了其在验证问题中的应用,并论证了量化消除本身可受益于结构感知计算。

摘要:

通常,硬件验证的实用算法通过应用于特定公式$F$来获得语义结果。也就是说,尽管该算法使用$F$的具体细节(有时无意中),其结果适用于所有与$F$逻辑等价的公式。我们称通过有意利用$F$的具体细节而获得语义结果的计算为结构感知计算(SAC)。或许,使用SAC可以更快地获得语义结果。我们展示部分量化消除(PQE),作为量化消除的推广,可以用于SAC。本文的目标是双重的。首先,我们通过三个验证问题(属性生成、等价性检查和模型检查)的例子,解释如何通过PQE进行SAC。其次,我们论证PQE求解本身也可以从SAC中受益。我们使用SAT求解来介绍一种技术,该技术也可应用于结构感知的PQE求解。

英文摘要:

Typically, a practical algorithm of hardware verification obtains a semantic result by being applied to a particular formula $F$. That is, although this algorithm uses the specifics of $F$ (sometimes inadvertently), its result holds for all formulas logically equivalent to $F$. We refer to computations that get a semantic result by intentionally exploiting the specifics of $F$ as structure-aware computing (SAC). Arguably, using SAC allows one to get a semantic result faster. We show that partial quantifier elimination (PQE), a generalization of quantifier elimination, can be used for SAC. The objective of this paper is twofold. First, we explain how SAC is performed by PQE by the example of three verification problems (property generation, equivalence checking and model checking). Second, we argue that PQE solving itself can benefit from SAC. We use SAT solving to introduce a technique that can be also applied in structure-aware PQE solving.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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