Machine Learning Clustering Techniques for Selective Mitigation of Critical Design Features

TL;DR

This paper introduces a machine learning clustering approach to efficiently identify and group circuit flip-flops by vulnerability, enabling targeted mitigation that balances reliability improvements with hardware overhead.

Contribution

It proposes a novel clustering methodology that reduces fault simulation costs by grouping similar flip-flops based on their susceptibility, streamlining the selective mitigation process.

Findings

Clustering effectively groups flip-flops with similar failure contributions.

Significant reduction in fault simulation time and cost.

Clustering results closely approximate exhaustive fault-injection mitigation.

Abstract

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.