针对抽象导向仿真的基于活动的细化

Debapriya Chatterjee, V. Bertacco
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引用次数: 2

摘要

半形式化验证工具越来越受欢迎,因为它们能够平衡逻辑模拟器的性能和形式化验证的目标集中功能。在这个领域中,基于抽象的仿真是一种已经在一些研究工作中提出的技术,也出现在一些商业解决方案中。基于抽象的仿真对设计抽象进行可达性分析,收集每个设计状态与目标状态的距离的近似信息,然后在逻辑模拟器的引导搜索中使用该信息。不幸的是,到目前为止,抽象的质量一直是这个半形式化解决方案中最薄弱的环节,因为它对使模拟器达到验证目标的影响很大。本文提出了一种在基于抽象的仿真框架中进行抽象细化的新解决方案。我们的解决方案在仿真期间收集切换活动信息,并根据对这些信息的分析确定如何修改和改进抽象。通过使用精化,工具制作的原始抽象不再是半形式化搜索的关键方面。相反,最初的抽象可能是弱的,随着时间的推移而改进以使模拟器达到目标状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Activity-based refinement for abstraction-guided simulation
Semi-formal verification tools are gaining popularity because of their ability to balance the performance of logic simulators with the goal-focused capabilities of formal verification. Within this domain, abstraction-based simulation is a technique that has been proposed in several research works and has also emerged in a few commercial solutions. Abstraction-based simulation performs reachability analysis on a design abstraction to gather approximate information on the distance of each design state from a goal state, and then uses this information in a guided search by the logic simulator. Unfortunately, so far, the quality of the abstraction has been the weakest link in this semi-formal solution, because of its impact in enabling a simulator to reach a verification goal. This paper presents a novel solution for abstraction refinement that operates in an abstraction-based simulation framework. Our solution collects switching activity information during simulation and determines how to modify and improve an abstraction based on analysis of this information. By using refinement, the original abstraction crafted by the tool is no longer a critical aspect of the semi-formal search. Instead, initially the abstraction may be weak, improving over time to enable the simulator to reach the goal state.
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