Guiding Formal Verification Orchestration Using Machine Learning Methods

Abstract

Typical modern HW designs include many blocks associated with thousands of design properties. Having today's commercial formal verifiers utilize a complementary set of state-of-art formal algorithms is a key in enabling the formal verification tools to successfully cope with verification problems of different sizes, types, and complexities. Formal engines orchestration is the methodology used to pick the most appropriate formal engine for a specific verification problem. It assures proper scheduling of the formal engines to minimize the time consumed to solve individual design verification problems, hence highly impacts the time required to verify the overall design properties. This work proposes the utilization of supervised machine learning classification techniques to guide the orchestration step by predicting the formal engines that should be assigned to a design property. Up to 16,500 formal verification runs on RTL designs and their properties are used to train the classifier to create a prediction model. The classifier assigns any new verification problem to an appropriate list of formal engines associated with a probability distribution over the set of engines classes. Our results indicate how the proposed model is able to improve the formal suite total run-time by up to 59% of its maximum allowable time improvement using multi-classification-based orchestration and to nominate with 88% accuracy the appropriate formal engines for new-to-verify HW designs.