Massively Parallel Continuous Local Search for Hybrid SAT Solving on GPUs

no code implementations • 29 Aug 2023 • Yunuo Cen, Zhiwei Zhang, Xuanyao Fong

Although state-of-the-art (SOTA) SAT solvers based on conflict-driven clause learning (CDCL) have achieved remarkable engineering success, their sequential nature limits the parallelism that may be extracted for acceleration on platforms such as the graphics processing unit (GPU).

Connection Pruning for Deep Spiking Neural Networks with On-Chip Learning

no code implementations • 9 Oct 2020 • Thao N. N. Nguyen, Bharadwaj Veeravalli, Xuanyao Fong

We applied our approach to a deep SNN with the Time To First Spike (TTFS) coding and has successfully achieved 2. 1x speed-up and 64% energy savings in the on-chip learning and reduced the network connectivity by 92. 83%, without incurring any accuracy loss.