  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
https://github.com/Brain-Inspired-Cognitive-Engine/Conversion_Burst.
