In this article, we propose a new paradigm for training spiking neural networks (SNNs), spike accumulation forwarding (SAF). It is known that SNNs are energy-efficient but difficult to train. Consequently, many researchers have proposed various methods to solve this problem, among which online training through time (OTTT) is a method that allows inferring at each time step while suppressing the memory cost. However, to compute efficiently on GPUs, OTTT requires operations with spike trains and weighted summation of spike trains during forwarding. In addition, OTTT has shown a relationship with the Spike Representation, an alternative training method, though theoretical agreement with Spike Representation has yet to be proven. Our proposed method can solve these problems; namely, SAF can halve the number of operations during the forward process, and it can be theoretically proven that SAF is consistent with the Spike Representation and OTTT, respectively. Furthermore, we confirmed the above contents through experiments and showed that it is possible to reduce memory and training time while maintaining accuracy.

翻译：本文提出了一种新的脉冲神经网络（SNN）训练范式——脉冲累积前向传播（SAF）。已知SNN具有高能效但难以训练的特点。为此，众多研究者提出了多种解决方法，其中在线时间训练（OTTT）是一种允许在每个时间步进行推理同时抑制内存开销的方法。然而，为在GPU上高效计算，OTTT在前向传播过程中需要对脉冲序列进行操作并进行加权求和。此外，虽然OTTT与替代训练方法脉冲表征（Spike Representation）之间存在关联，但两者的理论一致性尚未得到证明。本文提出的方法能解决上述问题：SAF可将前向传播过程中的运算量减半，并可理论证明SAF分别与脉冲表征及OTTT具有一致性。通过实验验证了以上内容，表明该方法在保持精度的同时能够减少内存占用和训练时间。