Spiking Neural Network (SNN) is a promising energy-efficient AI model when
implemented on neuromorphic hardware. However, it is a challenge to efficiently
train SNNs due to their non-differentiability. Most existing methods either
suffer from high latency (i.e., long simulation time steps), or cannot achieve
as high performance as Artificial Neural Networks (ANNs). In this paper, we
propose the Differentiation on Spike Representation (DSR) method, which could
achieve high performance that is competitive to ANNs yet with low latency.
First, we encode the spike trains into spike representation using (weighted)
firing rate coding. Based on the spike representation, we systematically derive
that the spiking dynamics with common neural models can be represented as some
sub-differentiable mapping. With this viewpoint, our proposed DSR method trains
SNNs through gradients of the mapping and avoids the common
non-differentiability problem in SNN training. Then we analyze the error when
representing the specific mapping with the forward computation of the SNN. To
reduce such error, we propose to train the spike threshold in each layer, and
to introduce a new hyperparameter for the neural models. With these components,
the DSR method can achieve state-of-the-art SNN performance with low latency on
both static and neuromorphic datasets, including CIFAR-10, CIFAR-100, ImageNet,
and DVS-CIFAR10.

本文提出了一种 Differentiation on Spike Representation (DSR) 方法，利用加权频率编码将射击轨迹编码为射击表示，然后通过对子可微映射的梯度进行 SNN 训练，避免了 SNN 训练中常见的非可微性问题，该方法在静态和神经形态数据集上具有低延迟和最先进的 SNN 性能。