Spiking neural network (SNN), as a brain-inspired energy-efficient neural
network, has attracted the interest of researchers. While the training of
spiking neural networks is still an open problem. One effective way is to map
the weight of trained ANN to SNN to achieve high reasoning ability. However,
the converted spiking neural network often suffers from performance degradation
and a considerable time delay. To speed up the inference process and obtain
higher accuracy, we theoretically analyze the errors in the conversion process
from three perspectives: the differences between IF and ReLU, time dimension,
and pooling operation. We propose a neuron model for releasing burst spikes, a
cheap but highly efficient method to solve residual information. In addition,
Lateral Inhibition Pooling (LIPooling) is proposed to solve the inaccuracy
problem caused by MaxPooling in the conversion process. Experimental results on
CIFAR and ImageNet demonstrate that our algorithm is efficient and accurate.
For example, our method can ensure nearly lossless conversion of SNN and only
use about 1/10 (less than 100) simulation time under 0.693$\times$ energy
consumption of the typical method. Our code is available at
this https URL.

探讨了 ANN 转换成 SNN 的过程中容易出现的问题，并提出了利用 Lateral Inhibition Pooling 和 Burst spikes 等方法加快推理过程并提高准确性的算法，在 CIFAR 和 ImageNet 数据集上实验取得了很高的效率和精度。

具有突发电位的脉冲神经网络的高效准确转换

Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes

The spiking neural networks (SNNs) are considered as one of the most
promising artificial neural networks due to their energy efficient computing
capability. Recently, conversion of a trained deep neural network to an SNN has
improved the accuracy of deep SNNs. However, most of the previous studies have
not achieved satisfactory results in terms of inference speed and energy
efficiency. In this paper, we propose a fast and energy-efficient information
transmission method with burst spikes and hybrid neural coding scheme in deep
SNNs. Our experimental results showed the proposed methods can improve
inference energy efficiency and shorten the latency.

本研究提出了一种利用突发脉冲和混合神经编码方案实现的快速高效信息传输方法，以提高深度脉冲神经网络的推理能效和降低延迟。