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. By leveraging the fixed nature of kernels during inference, we apply the gated one-to-all product (GOAP) algorithm to compute 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, control-free inter-layer execution. Implemented on an FPGA, our sparsity-aware output-channel dataflow streaming (SAOCDS) accelerator achieves 23.5 MS/s (approximately double the baseline throughput) on the RadioML 2016 dataset, while reducing dynamic power and maintaining comparable classification accuracy. These results demonstrate strong potential for real-time, low-power deployment in edge cognitive radio systems.

翻译：无线通信技术（包括5G、新兴的6G网络以及物联网的大规模部署）的快速发展，加强了对高效频谱利用的需求。自动调制分类（AMC）在认知无线电系统中发挥着至关重要的作用，它能够实时识别调制方案，从而实现动态频谱接入和干扰抑制。虽然深度神经网络（DNNs）提供了较高的分类精度，但其计算和能耗需求给实时边缘部署带来了挑战。脉冲神经网络（SNNs）具有事件驱动的特性，提供了固有的能效优势，但在有限的硬件资源下同时实现高吞吐量和低功耗仍然具有挑战性。本研究提出了一种针对AMC任务优化的稀疏感知SNN流式加速器。与利用稀疏性但吞吐量低的传统脉动阵列，或能实现高吞吐量但无法充分利用输入和权重稀疏性的流式架构不同，我们的设计整合了二者的优势。通过利用推理过程中卷积核固定的特性，我们应用门控一对多乘积（GOAP）算法，仅对非零的输入-权重交集进行计算。额外或空白的迭代被预先计算并嵌入到推理数据流中，消除了动态数据获取，实现了完全流水线化、无控制的层间执行。在FPGA上实现后，我们的稀疏感知输出通道数据流流式（SAOCDS）加速器在RadioML 2016数据集上实现了23.5 MS/s的吞吐量（约为基线吞吐量的两倍），同时降低了动态功耗并保持了可比的分类精度。这些结果表明了其在边缘认知无线电系统中实现实时、低功耗部署的强大潜力。