Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure

2022/07/01 by Xuan Zheng, Kerstin Eder, Zheng, Xuan +3
Computer Science · Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Real-time simulation and control systems #Software Engineering (cs.SE) #Software Testing and Debugging Techniques #VLSI and Analog Circuit Testing

paper · pdf · doi:10.48550/arxiv.2207.00445

openalex publication_date 2022/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Novel test selectors used in simulation-based verification have been shown to significantly accelerate coverage closure regardless of the number of coverage holes. This paper presents a configurable and highly-automated framework for novel test selection based on neural networks. Three configurations of this framework are tested with a commercial signal processing unit. All three convincingly outperform random test selection with the largest saving of simulation being 49.37% to reach 99.5% coverage. The computational expense of the configurations is negligible compared to the simulation reduction. We compare the experimental results and discuss important characteristics related to the performance of the configurations.

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