The testing and verification of a complex hardware or software system, such as modern integrated circuits found in everything from smartphones to servers, can be a difficult process. One of the most difficult and time-consuming tasks a verification team faces is reaching coverage closure, or hitting all events in the coverage space. Coverage-directed-generation (CDG), or the automatic generation of tests that can hit hard-to-hit coverage events, and thus provide coverage closure, holds the potential to save verification teams significant simulation resources and time. In this paper, we propose a new approach to the CDG problem by formulating the CDG problem as a noisy derivative free optimization problem. However, this formulation is complicated by the fact that derivatives of the objective function are unavailable, and the objective function evaluations are corrupted by noise. We solve this noisy optimization problem by utilizing techniques from direct optimization coupled with a robust noise estimator, and by leveraging techniques from inverse problems to estimate the gradient of the noisy objective function. We demonstrate the efficiency and reliability of this new approach through numerical experiments with a noised quadratic function and an abstract model of part of IBM’s NorthStar processor, a superscalar in-order processor designed for servers.

测试和验证复杂的硬件或软件系统，例如从智能手机到服务器的任何事物中都存在的现代集成电路，可能会很困难。验证团队面临的最困难和最耗时的任务之一是关闭覆盖范围，或击中覆盖空间中的所有事件。覆盖导向的一代（CDG）或自动生成可以击中难以解决的覆盖事件，从而提供覆盖关闭的测试，有可能节省验证团队大量的仿真资源和时间。在本文中，我们通过将CDG问题公式化为一个无噪声的无导数优化问题，提出了一种解决CDG问题的新方法。但是，由于无法获得目标函数的导数，并且噪声干扰了目标函数的评估，因此使这种表述变得复杂。我们通过利用直接优化技术与鲁棒噪声估计器相结合，并利用逆问题中的技术来估计噪声目标函数的梯度，来解决此噪声优化问题。NorthStar处理器，一种专为服务器设计的超标量顺序处理器。