HybMT: Hybrid Meta-Predictor based ML Algorithm for Fast Test Vector Generation

TL;DR

HybMT is a novel hybrid ML-based ATPG algorithm that significantly reduces test generation time and outperforms existing commercial and ML-based tools in speed while maintaining high fault coverage.

Contribution

This paper introduces HybMT, a hybrid meta-predictor ATPG algorithm combining neural networks and SVMs to enhance speed and accuracy in test vector generation for large circuits.

Findings

56.6% reduction in CPU time compared to commercial tools

126.4% speedup over previous ML-based algorithms

Maintains high fault coverage on benchmark circuits

Abstract

ML models are increasingly being used to increase the test coverage and decrease the overall testing time. This field is still in its nascent stage and up till now there were no algorithms that could match or outperform commercial tools in terms of speed and accuracy for large circuits. We propose an ATPG algorithm HybMT in this paper that finally breaks this barrier. Like sister methods, we augment the classical PODEM algorithm that uses recursive backtracking. We design a custom 2-level predictor that predicts the input net of a logic gate whose value needs to be set to ensure that the output is a given value (0 or 1). Our predictor chooses the output from among two first-level predictors, where the most effective one is a bespoke neural network and the other is an SVM regressor. As compared to a popular, state-of-the-art commercial ATPG tool, HybMT shows an overall reduction of 56.6% in the CPU time without compromising on the fault coverage for the EPFL benchmark circuits. HybMT also shows a speedup of 126.4% over the best ML-based algorithm while obtaining an equal or better fault coverage for the EPFL benchmark circuits.