Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure
This work addresses the need for faster verification processes in hardware design, specifically for signal processing units, though it appears incremental as it builds on existing novelty-based test selection methods.
The paper tackles the problem of accelerating functional coverage closure in simulation-based verification by introducing a neural network-based framework for novelty-based test selection, achieving up to 49.37% reduction in simulation time to reach 99.5% coverage.
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.