利用动态子句激活加速基于sat的ATPG

Stephan Eggersglüß, Daniel Tille, R. Drechsler
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引用次数: 15

摘要

基于sat的ATPG被证明是经典结构ATPG算法(如FAN)的鲁棒替代品。使用基于sat的ATPG方法可以显著减少未分类故障的数量。与结构ATPG相反,SAT求解器在合取范式(CNF)的布尔公式上工作。这导致SAT求解器在应用于ATPG时存在一些缺点,例如CNF转换时间和结构知识的丢失。因此,基于sat的ATPG算法对于难以测试的故障具有很强的鲁棒性,但对于易于测试的故障则存在较大的开销。为了减少结构化和基于SAT的ATPG算法之间的运行时间差距,同时保持高水平的鲁棒性,我们提出了SAT技术动态子句激活(DCA)。SAT求解器使用DCA对逻辑电路的部分公式进行求解,该部分公式在搜索过程中利用结构知识动态扩展。此外,有效的动态学习技术可以很容易地集成到所提出的技术中。该方法在大型工业电路中得到了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speeding up SAT-Based ATPG Using Dynamic Clause Activation
SAT-based ATPG turned out to be a robust alternative to classical structural ATPG algorithms such as FAN. The number of unclassified faults can be significantly reduced using a SAT-based ATPG approach. In contrast to structural ATPG, SAT solvers work on a Boolean formula in Conjunctive Normal Form (CNF). This results in some disadvantages for SAT solvers when applied to ATPG, e.g. CNF transformation time and loss of structural knowledge. As a result, SAT-based ATPG algorithms are very robust for hard-to-test faults, but suffer from the overhead for easy-to-test faults. We propose the SAT technique Dynamic Clause Activation (DCA) in order to reduce the run time gap between structural and SAT-based ATPG algorithms and, at the same time, retain the high level of robustness. Using DCA, the SAT solver works on a partial formula of a logic circuit which is dynamically extended during the search process using structural knowledge. Furthermore, efficient dynamic learning techniques can be easily integrated within the proposed technique. The approach is evaluated on large industrial circuits.
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