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DTSTAMP:20240626T180034Z
LOCATION:Level 2 Lobby
DTSTART;TZID=America/Los_Angeles:20240626T180000
DTEND;TZID=America/Los_Angeles:20240626T190000
UID:dac_DAC 2024_sess256_LBR119@linklings.com
SUMMARY:Late Breaking Results: Differential and Massively Parallel Samplin
 g of SAT Formulas
DESCRIPTION:Late Breaking Results Poster\n\nArash Ardakani, Minwoo Kang, K
 evin He, Vighnesh Iyer, Suhong Moon, and John Wawrzynek (University of Cal
 ifornia, Berkeley)\n\nDiverse solutions to the Boolean satisfiability (SAT
 ) problem are essential for thorough testing and verification of software 
 and hardware designs, ensuring reliability and applicability to real-world
  scenarios. We introduce a novel differentiable sampling method, called Di
 ffSampler, which employs gradient descent (GD) to learn diverse solutions 
 to the SAT problem. By formulating SAT as a supervised multi-output regres
 sion task and minimizing its loss function using GD, our approach enables 
 performing the learning operations in parallel, leading to GPU-accelerated
  sampling and comparable runtime performance w.r.t. heuristic samplers. We
  demonstrate that DiffSampler can generate diverse uniform-like solutions 
 similar to conventional samplers.
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