Abstract
In recent years, machine learning has demonstrated its potential in many challenging problems. In this paper, we extend its use to hardware formal property verification and propose DeepIC3, a method that takes advantage of graph learning in the classic IC3/PDR algorithm. In DeepIC3, graph neural networks are integrated to improve the result of local inductive generalization. This helps provide a global view of the state transition system and can potentially lead the algorithm out of local optima in the search of inductive invariants. Our experiments demonstrate that DeepIC3 accelerates the vanilla algorithm in nontrivial test cases of hardware model checking competition benchmarks (HWMCC2020) with up to 10.8x speed-up. The proposed machine-learning integration preserves soundness and is universally applicable to various IC3/PDR implementations.
Guangyu (Gary) Hu
Researcher at Huawei Hong Kong Research Center, Fermat Lab
My research interests include LLM + formal verification reasoning and hardware formal verification.