Towards Stable and Scalable Event-Driven Training of Spiking Neural Networks

Spiking neural networks (SNNs) offer a promising route towards energy-efficient deep learning on account of their sparse, event-driven computation. Activation-based training achieves competitive performance but does not exploit the event-driven computation during training, leading to memory inefficiency. In contrast, event-driven (time-based) training performs credit assignment with more efficient memory utilization but remains underexplored and has not been shown to scale favorably across architectures, datasets, and inference timesteps. In this work, we develop an event-driven training approach by identifying and addressing sources of instability in existing formulations, thereby enabling stable and scalable training across architectures and inference timesteps. Our approach retains the memory-efficient formulation of event-driven learning while enabling the effective use of batch normalization, which we show to be important for SNN training stability, without relying on ad-hoc methods such as supervisory spike injection used in previous approaches. We obtain state-of-the-art results among event-driven methods on both static and neuromorphic benchmarks, with a 5% improvement in accuracy at less than half the inference timesteps on CIFAR-100, and even extend event-driven training to Tiny-ImageNet. Overall, our results establish event-driven learning as a scalable and efficient alternative to activation-based training for deep SNNs.