用于关键设计特征选择性缓解的机器学习聚类技术
Machine Learning Clustering Techniques for Selective Mitigation of Critical Design Features
摘要：
选择性缓解或选择性硬化是一种有效的技术，以获得在电路的整体可靠性的改善和由硬化技术引起的硬件开销之间的良好权衡。选择性缓解依赖于根据其敏感性和关键性优先保护电路实例。然而，根据脆弱性对电路部件进行排名通常需要计算密集的故障注入模拟活动。本文提出了一种新的方法，它使用机器学习聚类技术组触发器具有类似的预期贡献的整体功能故障率，基于分析的一组紧凑的功能组合属性的静态元素和动态元素。故障模拟活动可以在每个组的基础上执行，大大减少了评估的时间和成本。通过一个实际的例子，评估了利用机器学习聚类算法对相似敏感触发器进行分组的有效性，并应用了不同的聚类算法，将结果与通过穷举故障注入仿真得到的理想选择性缓解进行了比较。
英文摘要：
Selective mitigation or selective hardening is an effective technique to obtain a good trade-off between the improvements in the overall reliability of a circuit and the hardware overhead induced by the hardening techniques. Selective mitigation relies on preferentially protecting circuit instances according to their susceptibility and criticality. However, ranking circuit parts in terms of vulnerability usually requires computationally intensive fault-injection simulation campaigns. This paper presents a new methodology which uses machine learning clustering techniques to group flip-flops with similar expected contributions to the overall functional failure rate, based on the analysis of a compact set of features combining attributes from static elements and dynamic elements. Fault simulation campaigns can then be executed on a per-group basis, significantly reducing the time and cost of the evaluation. The effectiveness of grouping similar sensitive flip-flops by machine learning clustering algorithms is evaluated on a practical example.Different clustering algorithms are applied and the results are compared to an ideal selective mitigation obtained by exhaustive fault-injection simulation.
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