Despite the rapid progress of neuromorphic computing, inadequate capacity and 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. In this paper, we first identify that this negligence leads to impeded information flow and the accompanying degradation problem in previous residual SNNs. To address this issue, we propose a novel SNN-oriented residual architecture termed MS-ResNet, which establishes membrane-based shortcut pathways, and further prove that the gradient norm equality can be achieved in MS-ResNet by introducing block dynamical isometry theory, which ensures the network can be well-behaved in a depth-insensitive way. Thus we are 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. To validate the effectiveness of MS-ResNet, experiments on both frame-based and neuromorphic datasets are conducted. MS-ResNet104 achieves a superior result of 76.02% accuracy on ImageNet, which is the highest to our best knowledge in the domain of directly trained SNNs. Great energy efficiency is also observed, with an average of only one spike per neuron needed to classify an input sample. We believe our powerful and scalable models will provide a strong support for further exploration of SNNs.

翻译：尽管神经形态计算取得了快速进展，但脉冲神经网络（SNNs）容量不足和表征能力有限的问题严重限制了其在实际中的应用范围。残差学习与捷径已被证明是训练深度神经网络的重要方法，但以往研究鲜少评估其对基于脉冲的通信和时空动力学特性的适用性。本文首先指出这种忽视导致了先前残差SNNs中信息流受阻及伴随的退化问题。为解决该问题，我们提出了一种新颖的面向SNN的残差架构MS-ResNet，该架构建立了基于膜电位（membrane-based）的捷径通路，并通过引入块动态等距理论进一步证明了MS-ResNet中可实现梯度范数相等性，从而确保网络能以深度不敏感的方式保持良好性能。由此我们能够显著扩展直接训练SNNs的深度，例如在CIFAR-10上达到482层、在ImageNet上达到104层，且未观察到任何轻微退化现象。为验证MS-ResNet的有效性，我们在基于帧和神经形态数据集上进行了实验。MS-ResNet104在ImageNet上实现了76.02%的优异准确率，据我们所知，这是直接训练SNNs领域中的最高结果。同时观察到显著的能效优势，分类每个输入样本平均仅需每个神经元发放一个脉冲。我们相信，这种强大且可扩展的模型将为SNNs的进一步探索提供有力支持。