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~$μ$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.

翻译：脉冲神经网络（SNNs）通过突触可塑性提供了一种生物可信的学习机制，无需反向传播的计算开销即可实现无监督自适应。为将这种能力应用于机器人技术，本文提出了FireFly-P，一种基于FPGA的硬件加速器，它实现了一种用于实时自适应控制的新型可塑性算法。通过利用片上可塑性，我们的架构增强了网络的泛化能力，确保了在动态和非结构化环境中的鲁棒性能。该硬件设计实现了推理和可塑性更新的端到端延迟仅为8~$μ$s，从而能够快速适应未见过的场景。FireFly-P在微型的Cmod A7-35T FPGA上实现，功耗仅为0.713~W，占用约10K~LUT，使其非常适合功耗和资源受限的嵌入式机器人平台。这项工作表明，硬件加速的SNN可塑性是实现自适应、低延迟和节能控制系统的一条可行路径。