FireFly-P: FPGA-Accelerated Spiking Neural Network Plasticity for Robust Adaptive Control

TL;DR

FireFly-P is an FPGA-based hardware accelerator for spiking neural networks that enables real-time, energy-efficient adaptive control in robotics through on-chip plasticity, achieving low latency and robustness in dynamic environments.

Contribution

This work introduces FireFly-P, a novel FPGA architecture implementing plasticity for SNNs, enabling fast, low-power adaptive control for robotics applications.

Findings

Achieves 8 microsecond latency for inference and plasticity updates.

Consumes only 0.713 W and ~10K LUTs on a Cmod A7-35T FPGA.

Demonstrates robust adaptive control in dynamic environments.

Abstract

Spiking Neural Networks (SNNs) offer a biologically plausible learning mechanism through synaptic plasticity, enabling unsupervised adaptation without the computational overhead of backpropagation. To harness this capability for robotics, this paper presents FireFly-P, an FPGA-based hardware accelerator that implements a novel plasticity algorithm for real-time adaptive control. By leveraging on-chip plasticity, our architecture enhances the network's generalization, ensuring robust performance in dynamic and unstructured environments. The hardware design achieves an end-to-end latency of just 8~$\mu$s for both inference and plasticity updates, enabling rapid adaptation to unseen scenarios. Implemented on a tiny Cmod A7-35T FPGA, FireFly-P consumes only 0.713~W and $\sim$10K~LUTs, making it ideal for power- and resource-constrained embedded robotic platforms. This work demonstrates that hardware-accelerated SNN plasticity is a viable path toward enabling adaptive, low-latency, and energy-efficient control systems.