High-Throughput SAT Sampling

In this work, we present a novel technique for GPU-accelerated Boolean satisfiability (SAT) sampling. Unlike conventional sampling algorithms that directly operate on conjunctive normal form (CNF), our method transforms the logical constraints of SAT problems by factoring their CNF representations i...

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Bibliographic Details
Published in:Proceedings - Design, Automation, and Test in Europe Conference and Exhibition pp. 1 - 7
Main Authors: Ardakani, Arash, Kang, Minwoo, He, Kevin, Huang, Qijing, Wawrzynek, John
Format: Conference Proceeding
Language:English
Published: EDAA 31.03.2025
Subjects:
Benchmark testing
Boolean functions
Boolean Satisfiability
Distance measurement
Gradient Descent
Logic
Multi-level Circuits
Optimization
Performance gain
Runtime
Sampling methods
Tensors
Testing
Transforms
Verification
ISSN:1558-1101
Online Access:Get full text
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Summary:In this work, we present a novel technique for GPU-accelerated Boolean satisfiability (SAT) sampling. Unlike conventional sampling algorithms that directly operate on conjunctive normal form (CNF), our method transforms the logical constraints of SAT problems by factoring their CNF representations into simplified multilevel, multi-output Boolean functions. It then leverages gradient-based optimization to guide the search for a diverse set of valid solutions. Our method operates directly on the circuit structure of refactored SAT instances, reinterpreting the SAT problem as a supervised multi-output regression task. This differentiable technique enables independent bit-wise operations on each tensor element, allowing parallel execution of learning processes. As a result, we achieve GPU-accelerated sampling with significant runtime improvements ranging from 33.6x to 523.6x over state-of-the-art heuristic samplers. We demonstrate the superior performance of our sampling method through an extensive evaluation on 60 instances from a public domain benchmark suite utilized in previous studies.
ISSN:1558-1101
DOI:10.23919/DATE64628.2025.10993248