Identifying Reliability-Critical Primary Inputs of Combinational Circuits Based on the Model of Gate-Sensitive Attributes
Identifying Reliability-Critical Primary Inputs of Combinational Circuits Based on the Model of Gate-Sensitive Attributes
复制标题
基于门敏感属性模型识别组合电路的可靠性关键主输入
DOI：
10.1109/tcad.2022.3142194
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发表时间：
2022-11-01
影响因子：
2.9
通讯作者：
Zhou, Qianwei
中科院分区：
文献类型：
--
作者：
Xiao, Jie;Chen, Wenbo;Zhou, Qianwei
可靠性关键主输入引线（RCPI）的识别在逻辑电路可靠性边界的测试和预测中起着重要的作用。本文提出了一种基于门敏感属性的方法来估计组合电路中主输入引线对其可靠性的关键性。面向输入向量，基于子电路的遍历方法标记电路中每个门的关键输入引线。门敏感属性和反向递归算法量化的影响，每个RCPI电路的可靠性下的输入向量。采用基于单输出子电路的并行计算方法，降低了计算复杂度，加快了计算速度。基于相似性的聚类避免了不必要的计算，并使用自适应策略来检查收敛性。在基准电路上的实验结果表明，该方法的平均精度为0.9634（以蒙特卡罗（MC）模型为参考），平均速度是MC模型的3445倍，而平均内存开销比MC模型多1.67。虽然通过其他参考方法获得的最差输入向量的适应度平均比该方法好1.09倍，但该方法平均比该参考方法快约21倍。
The identification of reliability-critical primary input leads (RCPIs) plays an important role in the testing and prediction of reliability boundaries of logic circuits. This article presents a gate-sensitive-attributes-based approach to estimate the criticality of the primary input leads in combinational circuits to their reliability. Oriented to the input vector, a subcircuit-based traversal method marks the critical input leads of each gate in a circuit. Gate-sensitive attributes and a reverse recursive algorithm quantify the effect of each RCPI on circuit reliability under the input vector. A parallel calculation method based on subcircuits with only one primary output reduces the computational complexity to accelerate the calculation process. Similarity-based clustering avoids unnecessary calculations, and a self-adaptive strategy is used to check convergence. Experimental results on benchmark circuits show that the average accuracy of this approach is 0.9634 with Monte Carlo (MC) as the reference and it is 3445 times faster than the MC on average while its average memory cost is 1.67 greater than the MC model. Although the fitness of the worst input vector obtained by other reference methods is 1.09 times better than that of this approach on average, this approach is approximately 21 times faster than that reference method on average.