Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods cannot simultaneously solve both training and inference dilemmas. This work proposes a novel Temporal Reversible architecture for SNNs (T-RevSNN) to jointly address the training and inference challenges by altering the forward propagation of SNNs. We turn off the temporal dynamics of most spiking neurons and design multi-level temporal reversible interactions at temporal turn-on spiking neurons, resulting in a $O(L)$ training memory. Combined with the temporal reversible nature, we redesign the input encoding and network organization of SNNs to achieve $O(1)$ inference energy cost. Then, we finely adjust the internal units and residual connections of the basic SNN block to ensure the effectiveness of sparse temporal information interaction. T-RevSNN achieves excellent accuracy on ImageNet, while the memory efficiency, training time acceleration, and inference energy efficiency can be significantly improved by $8.6 \times$, $2.0 \times$, and $1.6 \times$, respectively. This work is expected to break the technical bottleneck of significantly increasing memory cost and training time for large-scale SNNs while maintaining high performance and low inference energy cost. Source code and models are available at: https://github.com/BICLab/T-RevSNN.

翻译：受大脑启发的脉冲神经网络（SNNs）的多时间步仿真在训练期间显著增加了内存需求，并提高了推理能耗。当前的训练方法无法同时解决训练与推理两方面的难题。本研究提出一种新颖的时序可逆脉冲神经网络架构（T-RevSNN），通过改变SNN的前向传播方式，共同应对训练与推理的挑战。我们关闭了大多数脉冲神经元的时间动态，并在时序开启的脉冲神经元处设计了多级时序可逆交互，从而实现了$O(L)$的训练内存。结合时序可逆特性，我们重新设计了SNN的输入编码与网络组织方式，以实现$O(1)$的推理能耗。随后，我们对基础SNN模块的内部单元与残差连接进行了精细调整，以确保稀疏时序信息交互的有效性。T-RevSNN在ImageNet数据集上取得了优异的精度，同时其内存效率、训练时间加速与推理能效分别可显著提升$8.6 \times$、$2.0 \times$与$1.6 \times$。本工作有望在保持高性能与低推理能耗的同时，突破大规模SNN内存成本与训练时间显著增加的技术瓶颈。源代码与模型已发布于：https://github.com/BICLab/T-RevSNN。