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

• URL: http://arxiv.org/abs/2207.00445v3
• Date: Wed, 14 Jun 2023 21:53:05 GMT
• Title: Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure
• Authors: Xuan Zheng, Kerstin Eder and Tim Blackmore
• Abstract summary: This paper presents a 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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