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：本文提出了一种训练脉冲神经网络的新范式——脉冲累积前向传播（SAF）。众所周知，脉冲神经网络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案，其中在线时序训练（OTTT）方法能在每个时间步进行推理的同时抑制存储开销。然而，为在GPU上实现高效计算，OTTT在前向传播过程中需要对脉冲序列进行操作并计算脉冲序列的加权和。此外，尽管OTTT与另一种训练方法"脉冲表示"存在关联性，但两者之间的理论一致性尚未得到证明。本文提出的方法可解决上述问题：SAF能将前向传播过程中的运算量减半，并能从理论上证明SAF分别与脉冲表示和OTTT具有一致性。通过实验验证，我们证实了上述结论，并证明该方法在保持精度的同时能够降低存储开销和训练时间。