FVRuleLearner: Operator-Level Reasoning Tree (OP-Tree)-Based Rules Learning for Formal Verification
Summary: arXiv:2604.03245v1 Announce Type: cross
The integration of large language models (LLMs) into various domains has sparked interest in automating formal verification (FV). This vital process ensures the correctness of hardware through precise mathematical assertions. However, the traditional methods of translating natural language into SystemVerilog Assertions (NL-to-SVA) have proven to be labor-intensive and complex. Despite the advancements in LLMs, challenges remain in generating SVA due to insufficient training data and the inherent complexity of FV operators.
To address these pressing issues, we introduce FVRuleLearner, an innovative Operator-Level Rule (Op-Rule) learning framework that employs a novel Operator Reasoning Tree (OP-Tree). This framework models SVA generation as a structured and interpretable reasoning process. FVRuleLearner operates through two complementary phases:
- Training: In this phase, FVRuleLearner constructs an OP-Tree that breaks down the NL-to-SVA alignment into fine-grained, operator-aware questions. This involves combining reasoning paths that lead to the formulation of correct assertions.
- Testing: During testing, the system performs operator-aligned retrieval to fetch relevant reasoning traces from the constructed OP-Tree, allowing it to generate new rules for previously unseen specifications.
In comprehensive studies, FVRuleLearner has demonstrated significant improvements over existing state-of-the-art methodologies. Specifically, it achieved a remarkable increase of 3.95% in syntax correctness and an impressive 31.17% in functional correctness on average. Furthermore, the implementation of FVRuleLearner has resulted in a substantial reduction of 70.33% in SVA functional failures across various operator categories. This was validated through a functional taxonomy analysis, underscoring the effectiveness of the OP-Tree application in generating Op-Rules for previously unseen NL-to-SVA tasks.
The findings from these studies establish FVRuleLearner as a pioneering framework for domain-specific reasoning and rule learning in the realm of formal verification. By leveraging structured reasoning through OP-Trees, FVRuleLearner not only enhances the efficiency of assertion generation but also contributes to the overall reliability of hardware verification processes.
In conclusion, FVRuleLearner represents a significant advancement in automating formal verification, addressing the challenges of SVA generation with a novel approach that combines operator-level reasoning with effective learning strategies. This framework not only holds promise for improving the accuracy and reliability of hardware verification but also sets a new benchmark for future developments in the field.