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Counterexample-Guided Diagnosis

机译:强调引导诊断

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摘要

In this paper, we propose a counterexample-guided diagnosis approach to identify faults in circuit designs described as net-lists on the gate-level. Given a faulty net-list and a logic specification of the correct, intended behavior of the circuit, the diagnosis algorithm iteratively computes the exact set of fault candidates, i.e., a subset of the circuit's gates at which all counterexamples can be rectified. The algorithm equips SAT-based diagnosis with systematic counterexample generation. In each iteration, an over-approximation of the fault candidates is computed and a new counterexample is generated such that at least one of the fault candidates can be excluded in the next iteration. The algorithm terminates if no such counterexample exists and no remaining fault candidate can be excluded. The number of counterexample generated is not minimal and, thus, we additionally provide a counterexample reduction algorithm to post-process the set of generated counterexamples and obtain some insight in how many counterexamples are sufficient to exactly pinpoint a fault. We evaluate counterexamples-guided diagnosis for a set of benchmark circuits and provide a comparison to an exact algorithm that uses a state-of-the-art QSAT oracle. The accuracy of both algorithms is per design equal, whereas counterexample-guided diagnosis significantly outperforms the QSAT-based diagnosis algorithm.
机译:在本文中,我们提出了一种强调引导的诊断方法来识别电路设计中描述的栅极级别的净列表的故障。给定禁用的网列表和电路的正确预期行为的逻辑规范,诊断算法迭代地计算所有反例可以纠正电路门的精确故障候选集。该算法通过系统的反例生成提供基于SAT的诊断。在每次迭代中,计算故障候选的过度近似,并且生成新的反例,使得可以在下次迭代中排除至少一个故障候选。如果不存在此类反异行为,则算法终止,并且不能排除剩余故障候选。生成的反例数不是最小的,因此,我们另外提供了一个对生成的实体混凝体的组成的对缩减算法来处理生成的校正器集并获得一些洞察力足以精确地定位故障。我们评估对一组基准电路的指导诊断,并与使用最先进的QSAT Oracle的精确算法提供比较。两种算法的准确性是平等的,而对基于QSAT的诊断算法显着优于卓越的诊断。

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