Presentation
Predicting Lemmas in Generalization of IC3
DescriptionThe IC3 algorithm, also known as PDR, has made a significant impact in the field of safety model checking in recent years due to its high efficiency, scalability, and completeness. The most crucial component of IC3 is inductive generalization, which involves dropping variables one by one and is often the most time-consuming step. In this paper, we propose a novel approach to predict a possible minimal lemma before dropping variables by utilizing the counterexample to propagation (CTP). By leveraging this approach, we can avoid dropping variables if predict successfully. The comprehensive evaluation demonstrates a commendable success rate in lemma prediction and a significant performance improvement achieved by our proposed method.
Event Type
Research Manuscript
TimeThursday, June 2711:00am - 11:15am PDT
Location3004, 3rd Floor
EDA
Design Verification and Validation