HybMT: Hybrid Meta-Predictor based ML Algorithm for Fast Test Vector Generation
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.
Cite
@article{arxiv.2207.11312,
title = {HybMT: Hybrid Meta-Predictor based ML Algorithm for Fast Test Vector Generation},
author = {Shruti Pandey and Jayadeva and Smruti R. Sarangi},
journal= {arXiv preprint arXiv:2207.11312},
year = {2023}
}
Comments
6 pages, 5 figures and 5 tables. Changes from the previous version: We modified our novel neural network model "HybNN" with a skip connection and found a significant improvement in the fault coverage and runtime of our HybMT-based PODEM algorithm. We train on the smaller ISCAS'85 circuits, report the results for the EPFL benchmark circuits (most recent and up to 70X large)