Machine Learning Clustering Techniques for Selective Mitigation of Critical Design Features

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.

Machine Learning to Tackle the Challenges of Transient and Soft Errors in Complex Circuits

The Functional Failure Rate analysis of today's complex circuits is a difficult task and requires a significant investment in terms of human efforts, processing resources and tool licenses. Thereby, de-rating or vulnerability factors are a major instrument of failure analysis efforts. Usually computationally intensive fault-injection simulation campaigns are required to obtain a fine-grained reliability metrics for the functional level. Therefore, the use of machine learning algorithms to assist this procedure and thus, optimising and enhancing fault injection efforts, is investigated in this paper. Specifically, machine learning models are used to predict accurate per-instance Functional De-Rating data for the full list of circuit instances, an objective that is difficult to reach using classical methods. The described methodology uses a set of per-instance features, extracted through an analysis approach, combining static elements (cell properties, circuit structure, synthesis attributes) and dynamic elements (signal activity). Reference data is obtained through first-principles fault simulation approaches. One part of this reference dataset is used to train the machine learning model and the remaining is used to validate and benchmark the accuracy of the trained tool. The presented methodology is applied on a practical example and various machine learning models are evaluated and compared.

On the Estimation of Complex Circuits Functional Failure Rate by Machine Learning Techniques

De-Rating or Vulnerability Factors are a major feature of failure analysis efforts mandated by today's Functional Safety requirements. Determining the Functional De-Rating of sequential logic cells typically requires computationally intensive fault-injection simulation campaigns. In this paper a new approach is proposed which uses Machine Learning to estimate the Functional De-Rating of individual flip-flops and thus, optimising and enhancing fault injection efforts. Therefore, first, a set of per-instance features is described and extracted through an analysis approach combining static elements (cell properties, circuit structure, synthesis attributes) and dynamic elements (signal activity). Second, reference data is obtained through first-principles fault simulation approaches. Finally, one part of the reference dataset is used to train the Machine Learning algorithm and the remaining is used to validate and benchmark the accuracy of the trained tool. The intended goal is to obtain a trained model able to provide accurate per-instance Functional De-Rating data for the full list of circuit instances, an objective that is difficult to reach using classical methods. The presented methodology is accompanied by a practical example to determine the performance of various Machine Learning models for different training sizes.