Spiker-LL: An Energy-Efficient FPGA Accelerator Enabling Adaptive Local Learning in Spiking Neural Networks

TL;DR

Spiker-LL is an FPGA-based accelerator that enables energy-efficient, adaptive local learning in spiking neural networks, suitable for edge devices with minimal power and latency.

Contribution

It extends the open-source Spiker+ architecture with microarchitectural support for local learning, enabling on-device training with low overhead.

Findings

Achieves up to 93% accuracy on MNIST, F-MNIST, and DIGITS datasets.

Provides sub-millisecond inference latency.

Consumes less than 0.1 mJ per inference, DSP-free and scalable.

Abstract

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.