IB-Net: Initial Branch Network for Variable Decision in Boolean Satisfiability
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 a...
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Format | Journal Article |
Language | English |
Published |
06.03.2024
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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. |
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AbstractList | 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. |
Author | Tian, Guangji Zhen, Huiling Xiao, Wenyi Chan, Tsz Ho Yuan, Mingxuan Huang, Junhua |
Author_xml | – sequence: 1 givenname: Tsz Ho surname: Chan fullname: Chan, Tsz Ho – sequence: 2 givenname: Wenyi surname: Xiao fullname: Xiao, Wenyi – sequence: 3 givenname: Junhua surname: Huang fullname: Huang, Junhua – sequence: 4 givenname: Huiling surname: Zhen fullname: Zhen, Huiling – sequence: 5 givenname: Guangji surname: Tian fullname: Tian, Guangji – sequence: 6 givenname: Mingxuan surname: Yuan fullname: Yuan, Mingxuan |
BackLink | https://doi.org/10.48550/arXiv.2403.03517$$DView paper in arXiv |
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Snippet | Boolean Satisfiability problems are vital components in Electronic Design
Automation, particularly within the Logic Equivalence Checking process.
Currently,... |
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Title | IB-Net: Initial Branch Network for Variable Decision in Boolean Satisfiability |
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