APEX: A Dual-Sparsity Accelerator for Precise and Efficient SNN Inference

Abstract

Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to Artificial Neural Networks (ANNs), leveraging sparse accumulate operations in the place of power-hungry multiply-and-accumulate operations. ANN-SNN conversion is a widely adopted approach to realize deep SNNs with accuracy comparable to that of ANNs. The Quantization-Clip-Floor-Shift (QCFS) activation minimizes conversion error, yet requires a large number of inference timesteps to match the source ANN accuracy on real-world vision datasets. PASCAL addresses this by proposing the Precise ANN-SNN Conversion Integrate-and-Fire (PASC-IF) neuron, which guarantees mathematical equivalence between the converted SNN and the source ANN, thereby achieving ANN-equivalent accuracy at significantly reduced timesteps. Despite this algorithmic advancement, the hardware implications of deploying the PASC-IF neuron remain unexplored. In this work, we present APEX, a dual-sparsity SNN inference accelerator that integrates the PASC-IF neuron into the LoAS hardware framework. The three-stage PASC-IF datapath is realized as a fully combinational circuit with no additional latency cost. APEX exploits dual sparsity in both input spikes and weights through a fully temporal-parallel dataflow, enabling efficient sparse computation and reduced memory traffic. Across all evaluated models, the PASC-IF neuron on average achieves up to 3% higher accuracy than the standard IF neuron, with a power overhead of only 1.3%-5.4%, an area overhead of 2.1%-2.7%, and 40% energy reduction for best accuracy configurations.