Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks
This addresses the limited depth and representation power of SNNs for neuromorphic computing applications, representing a significant advance rather than an incremental improvement.
The paper tackles the degradation problem in deep spiking neural networks (SNNs) by proposing a novel residual block, enabling training of up to 482 layers on CIFAR-10 and 104 layers on ImageNet without degradation, achieving 76.02% accuracy on ImageNet and high energy efficiency with one spike per neuron.
Despite the rapid progress of neuromorphic computing, the inadequate depth and the resulting insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice. Residual learning and shortcuts have been evidenced as an important approach for training deep neural networks, but rarely did previous work assess their applicability to the characteristics of spike-based communication and spatiotemporal dynamics. This negligence leads to impeded information flow and the accompanying degradation problem. In this paper, we identify the crux and then propose a novel residual block for SNNs, which is able to significantly extend the depth of directly trained SNNs, e.g., up to 482 layers on CIFAR-10 and 104 layers on ImageNet, without observing any slight degradation problem. We validate the effectiveness of our methods on both frame-based and neuromorphic datasets, and our SRM-ResNet104 achieves a superior result of 76.02% accuracy on ImageNet, the first time in the domain of directly trained SNNs. The great energy efficiency is estimated and the resulting networks need on average only one spike per neuron for classifying an input sample. We believe our powerful and scalable modeling will provide a strong support for further exploration of SNNs.