Large Language Models (LLMs) have shown potential for solving mathematical tasks. We show that LLMs can be utilized to generate proofs by induction for hardware verification and thereby replace some of the manual work done by Formal Verification engineers and deliver industrial value. We present a neurosymbolic approach that includes two prompting frameworks to generate candidate invariants, which are checked using a formal, symbolic tool. Our results indicate that with sufficient reprompting, LLMs are able to generate inductive arguments for mid-size open-source RTL designs. For $87\%$ of our problem set, at least one of the prompt setups succeeded in producing a provably correct inductive argument.
翻译:大语言模型(LLMs)在解决数学任务方面已展现出潜力。本文论证了LLMs可用于生成硬件验证中的归纳证明,从而替代形式化验证工程师的部分手动工作并创造工业价值。我们提出一种神经符号方法,包含两个提示框架以生成候选不变量,这些不变量通过形式化符号工具进行验证。实验结果表明,在充分重复提示的条件下,LLMs能够为中等规模的开源RTL设计生成归纳论证。在我们的问题集中,至少有一种提示设置成功生成了可证明正确的归纳论证,占比达$87\\%$。