Deploying adaptive intelligence at the edge remains challenging due to the high computational and energy cost of training neural models. Spiking Neural Networks (SNNs) offer a promising alternative, but enabling on-device learning requires hardware-algorithm co-design. This paper presents SPIKER-LL, an FPGA-based SNN accelerator that extends the open-source Spiker+ inference architecture with efficient support for the STSF local learning rule. Through targeted microarchitectural extensions, SPIKER-LL performs inference and online learning with minimal overhead. Across MNIST, F-MNIST, and DIGITS, it achieves up to 93% accuracy, sub-millisecond latency, and less than 0.1 mJ per inference, while remaining DSP-free and highly scalable for edge-FPGA deployments.

翻译：在边缘部署自适应智能仍面临严峻挑战，主要源于训练神经模型的高计算与高能耗成本。脉冲神经网络（SNNs）提供了颇具前景的替代方案，但实现设备端学习需要硬件与算法的协同设计。本文提出SPIKER-LL——一种基于FPGA的SNN加速器，通过扩展开源Spiker+推理架构，高效支持STSF局部学习规则。通过针对性的微架构扩展，SPIKER-LL能以极低开销执行推理与在线学习。在MNIST、F-MNIST和DIGITS数据集上，该加速器实现了高达93%的准确率、亚毫秒级延迟以及每推理低于0.1 mJ的能耗，同时保持无需数字信号处理器（DSP-Free）特性，且对边缘FPGA部署具有高度可扩展性。