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research-article

NeuroSelect: Learning to Select Clauses in SAT Solvers

Authors:

Bei YuAuthors Info & Claims

DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference

Article No.: 131, Pages 1 - 6

https://doi.org/10.1145/3649329.3656250

Published: 07 November 2024 Publication History

Abstract

Modern SAT solvers depend on conflict-driven clause learning to avoid recurring conflicts. Deleting less valuable learned clauses is a crucial component of modern SAT solvers to ensure efficiency. However, a single clause deletion policy cannot guarantee optimal performance on all SAT instances. This paper introduces a new clause deletion metric to diversify existing clause deletion policies. Then, we propose to use machine learning to evaluate and select clause deletion policies adaptively based on the input instance. We show that our method can reduce the runtime of the state-of-the-art SAT solver Kissat by 5.8% on large industry benchmarks.

References

[1]

S. COOK, "The complexity of theorem proving procedure," in Proc. STOC, 1971.

[2]

P. Vaezipoor, G. Lederman, Y. Wu, R. Grosse, and F. Bacchus, "Learning clause deletion heuristics with reinforcement learning," in AITP, 2020.

[3]

B. Bünz and M. Lamm, "Graph neural networks and boolean satisfiability," arXiv preprint arXiv:1702.03592, 2017.

[4]

D. Selsam, M. Lamm, B. Benedikt, P. Liang, L. de Moura, D. L. Dill et al., "Learning a SAT solver from single-bit supervision," in Proc. ICLR, 2018.

[5]

D. Selsam and N. Bjørner, "Guiding high-performance SAT solvers with unsat-core predictions," in Proc. SAT, 2019.

[6]

V. Kurin, S. Godil, S. Whiteson, and B. Catanzaro, "Can Q-learning with graph networks learn a generalizable branching heuristic for a sat solver?" Proc. NIPS, vol. 33, pp. 9608--9621, 2020.

[7]

W. Zhang, Z. Sun, Q. Zhu, G. Li, S. Cai, Y. Xiong, and L. Zhang, "Nlocalsat: Boosting local search with solution prediction," arXiv preprint arXiv:2001.09398, 2020.

[8]

J. M. Han, "Enhancing sat solvers with glue variable predictions," arXiv preprint arXiv:2007.02559, 2020.

[9]

J. H. Liang, C. Oh, M. Mathew, C. Thomas, C. Li, and V. Ganesh, "Machine learning-based restart policy for CDCL SAT solvers," in Proc. SAT, 2018.

[10]

F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, "The graph neural network model," IEEE Transactions on Neural Networks (TNN), vol. 20, no. 1, pp. 61--80, 2008.

Digital Library

[11]

K. Xu, W. Hu, J. Leskovec, and S. Jegelka, "How powerful are graph neural networks?" in Proc. ICLR, 2018.

[12]

V. P. Dwivedi and X. Bresson, "A generalization of transformer networks to graphs," arXiv preprint arXiv:2012.09699, 2020.

[13]

D. Kreuzer, D. Beaini, W. Hamilton, V. Létourneau, and P. Tossou, "Rethinking graph transformers with spectral attention," Proc. NIPS, vol. 34, pp. 21 618--21 629, 2021.

[14]

F. Shi, C. Lee, M. K. Bashar, N. Shukla, S.-C. Zhu, and V. Narayanan, "Transformer-based machine learning for fast sat solvers and logic synthesis," arXiv preprint arXiv:2107.07116, 2021.

[15]

Z. Shi, M. Li, Y. Liu, S. Khan, J. Huang, H. Zhen, M. Yuan, and Q. Xu, "Satformer: Transformer-based UNSAT core learning," in Proc. ICCAD, 2023.

[16]

A. Biere and M. Fleury, "Gimsatul, IsaSAT and Kissat entering the SAT Competition 2022," in SAT Competition, 2022.

[17]

W. Wang, Y. Hu, M. Tiwari, S. Khurshid, K. McMillan, and R. Miikkulainen, "Neurocomb: Improving SAT solving with graph neural networks," arXiv preprint arXiv:2110.14053, 2021.

[18]

Q. Wu, W. Zhao, C. Yang, H. Zhang, F. Nie, H. Jiang, Y. Bian, and J. Yan, "Sgformer: Simplifying and empowering transformers for large-graph representations," in Proc. NIPS, 2023.

[19]

Z. Li, J. Guo, and X. Si, "G4SATBench: Benchmarking and advancing sat solving with graph neural networks," arXiv preprint arXiv:2309.16941, 2023.

Cited By

Zhen HKai SYin LLi HLi MTang ZHuang JLian YYuan MHuang Y(2024)Towards Smart Industrial Hardware Formal Verification2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617514(343-344)Online publication date: 10-May-2024
https://doi.org/10.1109/ISEDA62518.2024.10617514

Index Terms

NeuroSelect: Learning to Select Clauses in SAT Solvers
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Machine learning approaches
      1. Markov decision processes
      2. Neural networks
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference

June 2024

2159 pages

ISBN:9798400706011

DOI:10.1145/3649329

Chair:
Vivek De

Copyright © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

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SIGDA: ACM Special Interest Group on Design Automation
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New York, NY, United States

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Published: 07 November 2024

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DAC '24

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SIGDA

DAC '24: 61st ACM/IEEE Design Automation Conference

June 23 - 27, 2024

CA, San Francisco, USA

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

Zhen HKai SYin LLi HLi MTang ZHuang JLian YYuan MHuang Y(2024)Towards Smart Industrial Hardware Formal Verification2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617514(343-344)Online publication date: 10-May-2024
https://doi.org/10.1109/ISEDA62518.2024.10617514

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