Machine learning (ML) has been used to accelerate the closure of functional coverage in simulation-based verification. A supervised ML algorithm, as a prevalent option in the previous work, is used to bias the test generation or filter the generated tests. However, for missing coverage events, these algorithms lack the positive examples to learn from in the training phase. Therefore, the tests generated or filtered by the algorithms cannot effectively fill the coverage holes. This is more severe when verifying large-scale design because the coverage space is larger and the functionalities are more complex. This paper presents a configurable framework of test selection based on neural networks (NN), which can achieve a similar coverage gain as random simulation with far less simulation effort under three configurations of the framework. Moreover, the performance of the framework is not limited by the number of coverage events being hit. A commercial signal processing unit is used in the experiment to demonstrate the effectiveness of the framework. Compared to the random simulation, the framework can reduce up to 53.74% of simulation time to reach 99% coverage level.

翻译：机器学习( ML) 已被用于加速关闭模拟核查中功能覆盖。 受监督的 ML 算法作为先前工作中的一个普遍选项, 被用于偏向测试生成或过滤生成的测试。 但是, 对于缺失的覆盖事件, 这些算法缺乏从培训阶段学习的积极实例。 因此, 由算法生成或过滤的测试无法有效地填补覆盖洞。 当核查大型设计时, 难度更大, 因为覆盖空间较大, 功能也更为复杂 。 本文展示了一个基于神经网络( NN) 的可配置测试选择框架, 它可以实现类似随机模拟的覆盖增益, 而框架的三个配置下的模拟努力则少得多。 此外, 框架的性能并不局限于被打击的覆盖事件的数量。 在实验中使用商业信号处理器来证明框架的有效性。 与随机模拟相比, 框架可以将模拟时间的53.74%减少到99%的覆盖水平。