2000 character limit reached
IB-Net: Initial Branch Network for Variable Decision in Boolean Satisfiability (2403.03517v1)
Published 6 Mar 2024 in cs.AI
Abstract: Boolean Satisfiability problems are vital components in Electronic Design Automation, particularly within the Logic Equivalence Checking process. Currently, SAT solvers are employed for these problems and neural network is tried as assistance to solvers. However, as SAT problems in the LEC context are distinctive due to their predominantly unsatisfiability nature and a substantial proportion of UNSAT-core variables, existing neural network assistance has proven unsuccessful in this specialized domain. To tackle this challenge, we propose IB-Net, an innovative framework utilizing graph neural networks and novel graph encoding techniques to model unsatisfiable problems and interact with state-of-the-art solvers. Extensive evaluations across solvers and datasets demonstrate IB-Net's acceleration, achieving an average runtime speedup of 5.0% on industrial data and 8.3% on SAT competition data empirically. This breakthrough advances efficient solving in LEC workflows.
- Learning to solve circuit-sat: An unsupervised differentiable approach. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, 2019.
- On the Glucose SAT solver. International Journal on Artificial Intelligence Tools (IJAIT), 27(1):1–25, 2018.
- Learning to solve smt formulas. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018.
- Proceedings of SAT Competition 2022: Solver and Benchmark Descriptions. Department of Computer Science Series of Publications B. Department of Computer Science, University of Helsinki, Finland, 2022.
- Gimsatul, IsaSAT and Kissat entering the SAT Competition 2022. In Tomas Balyo, Marijn Heule, Markus Iser, Matti Järvisalo, and Martin Suda, editors, Proc. of SAT Competition 2022 – Solver and Benchmark Descriptions, volume B-2022-1 of Department of Computer Science Series of Publications B, pages 10–11. University of Helsinki, 2022.
- Armin Biere. Cadical at the sat race 2019. 2019.
- Stephen A. Cook. The complexity of theorem-proving procedures. In Proceedings of the Third Annual ACM Symposium on Theory of Computing, STOC ’71, page 151–158, New York, NY, USA, 1971. Association for Computing Machinery.
- Accelerate sat-based atpg via preprocessing and new conflict management heuristics. In ASP-DAC 2022.
- Learning to select cuts for efficient mixed-integer programming. Pattern Recognition, 123:108353, 2022.
- Cassandra: Proactive conflict minimization through optimized task scheduling. In ICSE 2023, pages 732–741, 2013.
- K. Rustan M. Leino. Dafny: An automatic program verifier for functional correctness. In Edmund M. Clarke and Andrei Voronkov, editors, Logic for Programming, Artificial Intelligence, and Reasoning, pages 348–370, Berlin, Heidelberg, 2010. Springer Berlin Heidelberg.
- Combinatorial optimization with graph convolutional networks and guided tree search. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, page 537–546, Red Hook, NY, USA, 2018. Curran Associates Inc.
- Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2):318–327, 2020.
- A survey of graph neural networks for electronic design automation. In 2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD (MLCAD), pages 1–6, 2021.
- Guiding high-performance sat solvers with unsat-core predictions. In Mikoláš Janota and Inês Lynce, editors, Theory and Applications of Satisfiability Testing – SAT 2019, pages 336–353, Cham, 2019. Springer International Publishing.
- Learning a SAT solver from single-bit supervision. In ICLR 2019. OpenReview.net, 2019.
- Minisat v1.13 - a sat solver with conflict-clause minimization. 2005.
- G. S. Tseitin. On the Complexity of Derivation in Propositional Calculus, pages 466–483. Springer Berlin Heidelberg, Berlin, Heidelberg, 1983.
- Drat-trim: Efficient checking and trimming using expressive clausal proofs. In Carsten Sinz and Uwe Egly, editors, Theory and Applications of Satisfiability Testing – SAT 2014, pages 422–429, Cham, 2014. Springer International Publishing.
- Towards effective deep learning for constraint satisfaction problems. In John Hooker, editor, Principles and Practice of Constraint Programming, pages 588–597, Cham, 2018. Springer International Publishing.
- Nlocalsat: Boosting local search with solution prediction. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, 2021.
- Wgcn: Graph convolutional networks with weighted structural features. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’21, page 624–633, New York, NY, USA, 2021. Association for Computing Machinery.