STOP: spatiotemporal orthogonal propagation for weight-threshold-leakage synergistic training of deep spiking neural networks

Abstract The prevailing of artificial intelligence-of-things calls for higher energy-efficient edge computing paradigms, such as neuromorphic agents leveraging brain-inspired spiking neural network (SNN) models based on spatiotemporally sparse binary spikes. However, the lack of efficient and high-accuracy deep SNN learning algorithms prevents them from practical deployments at a strictly bounded cost. In this paper, we propose the spatiotemporal orthogonal propagation (STOP) algorithm framework to tackle this challenge. In the STOP framework, spatially-backward neuronal errors and temporally-forward traces propagate orthogonally and independently, mitigating the huge memory requirement for storing neural states across all time-steps and simplifying the computational flow. Furthermofe, the STOP framework enables fully synergistic learning of synaptic weights, firing thresholds, and leakage factors to improve SNN accuracy. Our STOP algorithm obtained high recognition accuracies of 94.84%, 74.92%, 98.26% and 77.10% on the CIFAR-10, CIFAR-100, DVS-Gesture and DVS-CIFAR10 datasets with adequate deep convolutional SNNs of VGG-11 or ResNet-18 structures. Compared with other deep SNN training algorithms, our method is more plausible for edge intelligent scenarios where resources are limited but high-accuracy in-situ learning is desired.