ICP-RL: Identifying Critical Paths for Fault Diagnosis Using Reinforcement Learning
ICP-RL: Identifying Critical Paths for Fault Diagnosis Using Reinforcement Learning
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DOI：
10.1145/3610294
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发表时间：
2023-07
影响因子：
1.4
通讯作者：
Jie Xiao;Yingying Ge;Ru Wang;Jungang Lou
中科院分区：
文献类型：
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作者：
Jie Xiao;Yingying Ge;Ru Wang;Jungang Lou
识别关键路径对于降低逻辑电路性能分析和可靠性计算的复杂性至关重要。在本文中，我们提出了一种利用强化学习框架来识别组合电路中关键路径的方法，以增强其适用性和兼容性。最初，我们根据电路结构信息配置模型的学习环境，为及时决策提供有价值的信息。随后，采用树的置信度上限(UCT)算法构造模型的行为决策策略，避免了无效遍历，降低了计算代价。然后，根据距离回路一次输出的距离，构造了面向目标的奖惩函数。最后，基于并行计算策略，构造了一种基于有限采样的自适应训练方法，提高了模型的预测精度，加快了收敛速度，提高了模型的质量。在基准电路上的实验结果表明，以功能时序分析方法为参考，该方法的平均精度高达99.39%，单次平均计算速度是参考方法的18.07倍。与蒙特卡罗模型相比，该方法具有更高的关键路径命中率，平均计算速度快928.75倍。
Identifying the critical paths is crucial to reducing the complexity of performance analysis and reliability calculation for logic circuits. In this article, we propose a method for identifying the critical path in a combination circuit using a reinforcement learning framework to enhance its applicability and compatibility. Initially, we configured the learning environment of the model based on circuit structure information to provide valuable information for decision-making on time. Subsequently, the upper confidence bound applied to trees (UCT) algorithm is employed to construct the behavior decision strategy of the model, which avoids invalid traversal and reduces computing costs. Then, a goal-oriented reward and punishment function is constructed based on the distance from the circuit primary outputs. Finally, based on the parallel computing strategy, we construct an adaptive training method to improve the model’s prediction accuracy by using finite sampling, which speeds up the convergence speed and enhances the quality of the model. Experimental results on benchmark circuits show that, with the functional timing analysis method as the reference, the average accuracy of the proposed method is as high as 99.39% and the single average calculation speed is 18.07 times faster than that of the reference method. Compared with the Monte Carlo model, the proposed method has a higher critical path hit rate, and the average calculation speed is 928.75 times faster.