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Spiking neural networks (SNNs) that enables energy efficient implementationon emerging neuromorphic hardware are gaining more attention. Yet now, SNNshave not shown competitive performance compared with artificial neural networks(ANNs), due to the lack of effective learning algorithms and efficientprogramming frameworks. We address this issue from two aspects: (1) We proposea neuron normalization technique to adjust the neural selectivity and develop adirect learning algorithm for deep SNNs. (2) Via narrowing the rate codingwindow and converting the leaky integrate-and-fire (LIF) model into anexplicitly iterative version, we present a Pytorch-based implementation methodtowards the training of large-scale SNNs. In this way, we are able to traindeep SNNs with tens of times speedup. As a result, we achieve significantlybetter accuracy than the reported works on neuromorphic datasets (N-MNIST andDVS-CIFAR10), and comparable accuracy as existing ANNs and pre-trained SNNs onnon-spiking datasets (CIFAR10). {To our best knowledge, this is the first workthat demonstrates direct training of deep SNNs with high performance onCIFAR10, and the efficient implementation provides a new way to explore thepotential of SNNs.

Direct Training for Spiking Neural Networks: Faster, Larger, Better