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

2026-01-29

AI Summary

This work proposes FireFly-P, an FPGA-based spiking neural network (SNN) hardware accelerator designed to address the challenge of achieving both low power consumption and real-time adaptability in dynamic, unstructured environments where traditional robotic control methods fall short. FireFly-P is the first implementation to efficiently integrate SNN inference with on-chip unsupervised synaptic plasticity mechanisms on a resource-constrained embedded platform (Cmod A7-35T). The design achieves an end-to-end latency of just 8 microseconds while consuming only 0.713 W of power and approximately 10K LUTs. This significant reduction in computational overhead and energy usage markedly enhances the robot’s real-time adaptability and robustness when operating in unknown environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Spiking Neural Networks
• synaptic plasticity
• adaptive control
• FPGA acceleration
• embedded robotics

Innovation

• Spiking Neural Networks
• Synaptic Plasticity
• FPGA Acceleration
• Real-time Adaptive Control
• Low-latency Inference