Spiking neural networks (SNNs) are a promising paradigm for energy-efficient event-driven computation, but large-scale SNN execution remains challenging because sparse spike communication and synchronization can dominate runtime. Existing solutions across CPU, GPU, ASIC, and FPGA platforms offer different trade-offs between programmability, efficiency, and scalability. To address this gap, we present NeuroRing, a modular and scalable SNN accelerator based on a stream-dataflow architecture and a bidirectional ring topology, implemented in High-Level Synthesis (HLS) on programmable FPGAs. NeuroRing supports modular single- and multi-FPGA deployment and is compatible with existing SNN workflows through integration with the NEST simulator. We evaluate NeuroRing on the cortical microcircuit benchmark and a Sudoku constraint-satisfaction workload. Results show that NeuroRing preserves the key activity statistics of the NEST reference model, achieves faster-than-real-time execution of the full-scale cortical microcircuit with a real-time factor (RTF) of 0.83, exhibits meaningful strong and weak scaling, and provides competitive energy efficiency on two programmable FPGAs. These results position NeuroRing as a flexible and scalable platform for both neuroscience simulation and broader event-driven applications.

翻译：脉冲神经网络（SNN）凭借其事件驱动的高能效计算范式展现出巨大潜力，但大规模SNN的执行仍面临挑战，因为稀疏脉冲通信与同步可能主导运行时间。现有基于CPU、GPU、ASIC和FPGA平台的解决方案在可编程性、效率与可扩展性之间提供了不同的权衡。为弥补这一不足，我们提出NeuroRing——一种基于流数据流架构与双向环形拓扑的模块化可扩展SNN加速器，并在可编程FPGA上通过高层次综合（HLS）实现。NeuroRing支持模块化的单/多FPGA部署，并通过与NEST仿真器的集成兼容现有SNN工作流。我们以皮层微电路基准测试及数独约束满足工作负载对NeuroRing进行评估。结果表明，NeuroRing保留了NEST参考模型的关键活动统计特征，以0.83的实时因子（RTF）实现了全尺寸皮层微电路的超实时执行，展现了显著的高强扩展性与弱扩展性，并在两块可编程FPGA上提供了具有竞争力的能效。这些成果使NeuroRing成为适用于神经科学仿真及更广泛事件驱动应用的灵活可扩展平台。