Abstract
Though graph neural networks (GNNs) have been used in SAT solution prediction, for a subset of symmetric SAT problems, we unveil that the current GNN-based end-to-end SAT solvers are bound to yield incorrect outcomes as they are unable to break symmetry in variable assignments. In response, we introduce AsymSAT, a new GNN architecture coupled where a recurrent neural network is (RNN) to produce asymmetric models. Moreover, we bring up a method to integrate machine-learning-based SAT assignment prediction with classic SAT solvers and demonstrate its performance on non-trivial SAT instances including logic equivalence checking and cryptographic analysis problems with as much as 75.45% time saving.
Zhiyuan Yan
Ph.D. student in Microelectronics Thrust
My research interests include hardware formal verification, AI for EDA and Boolean Satisfiability Problem.