Spiking Neural Networks (SNNs) that operate in an event-driven manner and employ binary spike representation have recently emerged as promising candidates for energy-efficient computing. However, a cost bottleneck arises in obtaining high-performance SNNs: training a SNN model requires a large number of time steps in addition to the usual learning iterations, hence this limits their energy efficiency. This paper proposes a general training framework that enhances feature learning and activation efficiency within a limited time step, providing a new solution for more energy-efficient SNNs. Our framework allows SNN neurons to learn robust spike feature from different receptive fields and update neuron states by utilizing both current stimuli and recurrence information transmitted from other neurons. This setting continuously complements information within a single time step. Additionally, we propose a projection function to merge these two stimuli to smoothly optimize neuron weights (spike firing threshold and activation). We evaluate the proposal for both convolution and recurrent models. Our experimental results indicate state-of-the-art visual classification tasks, including CIFAR10, CIFAR100, and TinyImageNet.Our framework achieves 72.41% and 72.31% top-1 accuracy with only 1 time step on CIFAR100 for CNNs and RNNs, respectively. Our method reduces 10x and 3x joule energy than a standard ANN and SNN, respectively, on CIFAR10, without additional time steps.

翻译：脉冲神经网络（SNN）采用事件驱动机制和二进制脉冲表征，近年来已成为实现低能耗计算的有前景候选方案。然而，获取高性能SNN存在成本瓶颈：除常规学习迭代外，训练SNN模型还需大量时间步长，这限制了其能效。本文提出一种通用训练框架，能在有限时间步长内增强特征学习与激活效率，为构建更高能效的SNN提供新方案。该框架允许SNN神经元从不同感受野学习鲁棒的脉冲特征，并利用当前刺激与其他神经元传递的循环信息更新神经元状态。该设置可在单个时间步长内持续补充信息。此外，我们提出投影函数融合这两种刺激，以平滑优化神经元权重（脉冲发放阈值与激活函数）。我们分别对卷积模型和循环模型进行了评估。实验结果表明，在CIFAR10、CIFAR100和TinyImageNet视觉分类任务中达到最优水平。在CIFAR100上，仅需1个时间步长，我们的框架在CNN和RNN上分别获得72.41%和72.31%的top-1准确率。在CIFAR10上，无需额外时间步长，本方法能耗比标准ANN和SNN分别降低10倍和3倍。