Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing owing to their low energy consumption during feedforward inference on neuromorphic hardware. However, it remains an open challenge how to effectively benefit from the sparse event-driven property of SNNs to minimize backpropagation learning costs. In this paper, we conduct a comprehensive examination of the existing event-driven learning algorithms, reveal their limitations, and propose novel solutions to overcome them. Specifically, we introduce two novel event-driven learning methods: the spike-timing-dependent event-driven (STD-ED) and membrane-potential-dependent event-driven (MPD-ED) algorithms. These proposed algorithms leverage precise neuronal spike timing and membrane potential, respectively, for effective learning. The two methods are extensively evaluated on static and neuromorphic datasets to confirm their superior performance. They outperform existing event-driven counterparts by up to 2.51% for STD-ED and 6.79% for MPD-ED on the CIFAR-100 dataset. In addition, we theoretically and experimentally validate the energy efficiency of our methods on neuromorphic hardware. On-chip learning experiments achieved a remarkable 30-fold reduction in energy consumption over time-step-based surrogate gradient methods. The demonstrated efficiency and efficacy of the proposed event-driven learning methods emphasize their potential to significantly advance the fields of neuromorphic computing, offering promising avenues for energy-efficiency applications.

翻译：受大脑启发的脉冲神经网络（SNNs）因在神经形态硬件上前馈推理时的低能耗而在神经形态计算领域备受关注。然而，如何有效利用 SNNs 的稀疏事件驱动特性来最小化反向传播学习成本仍是一个未解决的挑战。本文全面审视了现有事件驱动学习算法，揭示了其局限性，并提出了克服这些局限性的创新解决方案。具体而言，我们引入了两种新型事件驱动学习方法：脉冲时序依赖事件驱动（STD-ED）算法和膜电位依赖事件驱动（MPD-ED）算法。这两种方法分别利用精确的神经元脉冲时序和膜电位进行高效学习。通过在静态数据集和神经形态数据集上的广泛评估，确认了其卓越性能。在CIFAR-100数据集上，STD-ED 和 MPD-ED 方法分别比现有事件驱动算法提升了高达 2.51% 和 6.79% 的性能。此外，我们从理论和实验两方面验证了该方法在神经形态硬件上的能效优势。片上学习实验实现了比基于时间步长的替代梯度方法低 30 倍的能耗降低。所提出的事件驱动学习方法的效率和有效性凸显了其显著推动神经形态计算领域发展的潜力，为能效型应用开辟了有前景的途径。