Presentation
Spiker-LL: An Energy-Efficient FPGA Accelerator Enabling Adaptive Local Learning in Spiking Neural Networks
DescriptionDeploying adaptive intelligence at the edge remains challenging due to the high computational and energy cost of training neural models. SNNs offer a promising alternative, but enabling on-device learning requires hardware–algorithm co-design. This paper presents SpikerLL, an FPGA-based SNNs accelerator that extends the open-source Spiker+ inference architecture with efficient support for the STSF local learning rule. Through targeted microarchitectural extensions, SpikerLL performs inference and online learning with minimal overhead. Across MNIST, F-MNIST, and DIGITS, it achieves up to 93% accuracy, sub-millisecond latency, and <0.1 mJ per inference, while remaining DSP-free and highly scalable for edge-FPGA deployments.
Event Type
Work in Progress
TimeMonday, July 276:41pm - 6:41pm PDT
LocationExhibit Hall