Comperity: A Stateless Spiking Neural Network Accelerator with Differential Spike Encoding

Spiking Neural Networks (SNNs) leverage discrete, event-driven processing to achieve exceptional energy efficiency through intrinsic bit-sparsity. While current SNN accelerators effectively reduce latency by skipping zero-valued spikes, they often overlook the structural redundancy present in spatially adjacent activation patterns, or rely on expensive global search mechanisms to exploit it. We observe that neighboring spike rows frequently exhibit high similarity due to spatial locality. To exploit this efficiently, we introduce Differential Spike Encoding (DSE) , a logic-driven paradigm that decomposes adjacent rows into a shared Base Vector and sparse Differential Vectors . We present Comperity , a specialized hardware accelerator that implements a stateless, index-driven dataflow . By replacing complex pattern matching with lightweight AND/XOR logic arrays, Comperity significantly reduces arithmetic redundancy with negligible control overhead . Experimental results demonstrate that Comperity achieves speedups of 1.62 × , 9.0 × , and 2.3 × over state-of-the-art SNN accelerators Prosperity, PTB, and the A100 GPU on SpikeBERT projection layers, respectively. Moreover, Comperity improves geometric mean energy efficiency by 1.35 × , 10.7 × , and 270 × , respectively. Finally, analytical comparisons confirm that Comperity remains within a competitive range in per-synaptic-operation energy against event-driven neuromorphic platforms (e.g., Loihi and TrueNorth), providing a high-throughput and energy-efficient solution for edge neuromorphic computing.