论文标题
基于模拟的验证中的混合智能测试
Hybrid Intelligent Testing in Simulation-Based Verification
论文作者
论文摘要
基于仿真的硬件验证的有效测试具有挑战性。使用受限的随机测试生成，可能需要数百万个测试才能实现覆盖目标。绝大多数测试并不促进覆盖率进度，但它们消耗了验证资源。在本文中，我们提出了一种混合智能测试方法，结合了两种先前已分别处理的方法，即覆盖指导的测试选择和新颖性驱动的验证。以覆盖范围为导向的测试选择从覆盖反馈到偏置测试到最有效的测试。新颖性驱动的验证学会了识别和模拟与先前刺激不同的刺激，从而减少了模拟的数量并提高了测试效率。我们讨论了每种方法的优势和局限性，并展示了我们的方法如何解决每种方法的局限性，从而导致高效且有效的硬件测试。
Efficient and effective testing for simulation-based hardware verification is challenging. Using constrained random test generation, several millions of tests may be required to achieve coverage goals. The vast majority of tests do not contribute to coverage progress, yet they consume verification resources. In this paper, we propose a hybrid intelligent testing approach combining two methods that have previously been treated separately, namely Coverage-Directed Test Selection and Novelty-Driven Verification. Coverage-Directed Test Selection learns from coverage feedback to bias testing toward the most effective tests. Novelty-Driven Verification learns to identify and simulate stimuli that differ from previous stimuli, thereby reducing the number of simulations and increasing testing efficiency. We discuss the strengths and limitations of each method, and we show how our approach addresses each method's limitations, leading to hardware testing that is both efficient and effective.
