A5083333774- Formal Methods in Verification
- Software Testing and Debugging Techniques
- Distributed and Parallel Computing Systems
- Machine Learning and Algorithms
- Constraint Satisfaction and Optimization
- Model-Driven Software Engineering Techniques
- Machine Learning in Materials Science
- Data Mining Algorithms and Applications
- Parallel Computing and Optimization Techniques
University of California, Berkeley
2024-2025
Efficient sampling of satisfying formulas for circuit satisfiability (CircuitSAT), a well-known NP-complete problem, is essential in modern front-end applications thorough testing and verification digital circuits. Generating such samples hard computational problem due to the inherent complexity circuits, size search space, resource constraints involved process. Addressing these challenges has prompted development specialized algorithms that heavily rely on heuristics. However,...
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 multi-level, multi-output functions. It then leverages gradient-based optimization to guide search diverse set valid solutions. Our operates circuit structure refactored instances,...