论文标题
使用神经网络进行新颖的测试选择来加速功能覆盖范围
Using Neural Networks for Novelty-based Test Selection to Accelerate Functional Coverage Closure
论文作者
论文摘要
在基于模拟的验证中使用的新型测试选择器已显示出可显着加速覆盖范围，而不管覆盖孔的数量多少。本文提出了一个基于神经网络的新型测试选择的可配置且高度自动化的框架。使用商业信号处理单元测试了该框架的三种配置。所有这三个令人信服的胜过随机测试的选择，模拟节省的最大节省为49.37％，达到99.5％的覆盖范围。与模拟降低相比，配置的计算费用可以忽略不计。我们比较实验结果，并讨论与配置性能相关的重要特征。
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.
