Sparsity-Aware Streaming SNN Accelerator With Output-Channel Dataflow for Automatic Modulation Classification

The rapid advancement of wireless communication technologies, including 5G, emerging 6G networks, and the large scale deployment of the Internet of Things (IoT), has intensified the need for efficient spectrum utilization. Automatic modulation classification (AMC) plays a vital role in cognitive radio systems by enabling real-time identification of modulation schemes for dynamic spectrum access and interference mitigation. While deep neural networks (DNNs) offer high classification accuracy, their computational and energy demands pose challenges for real time edge deployment. Spiking neural networks (SNNs), with their event-driven nature, offer inherent energy efficiency, but achieving both high throughput and low power under constrained hardware resources remains challenging. This work proposes a sparsity-aware SNN streaming accelerator optimized for AMC tasks. Unlike traditional systolic arrays that exploit sparsity but suffer from low throughput, or streaming architectures that achieve high throughput but cannot fully utilize input and weight sparsity, our design integrates both advantages. Specifically, we adopt an output-channel dataflow strategy that enables workload balancing. By leveraging the fixed nature of kernels during inference, we apply the gated one-to-all product (GOAP) algorithm to exploit both temporal and spatial sparsity, computing only on non-zero input-weight intersections. Extra or empty iterations are precomputed and embedded into the inference dataflow, eliminating dynamic data fetches and enabling fully pipelined, fixed streaming order inter-layer execution. Implemented on a field-programmable gate array (FPGA), our sparsity-aware outputchannel dataflow streaming (SAOCDS) accelerator achieves 23.5 MS/s (approximately double the baseline throughput) on the RadioML 2016 dataset, while reducing dynamic power by 58.7% and maintaining comparable classification accuracy. These results demonstrate strong potential for real-time, low-power deployment in edge cognitive radio systems.