Spiker-LL: An Energy-Efficient FPGA Accelerator Enabling Adaptive Local Learning in Spiking Neural Networks
For edge AI applications requiring adaptive learning, this work provides an energy-efficient hardware solution, though it is incremental as it extends an existing architecture with a known learning rule.
Spiker-LL is an FPGA accelerator for spiking neural networks that enables on-device local learning with minimal energy overhead. It achieves up to 93% accuracy on MNIST, F-MNIST, and DIGITS, with sub-millisecond latency and less than 0.1 mJ per inference.
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.