  Spiking Neural Networks (SNNs) have emerged as a promising third generation
of neural networks, offering unique characteristics such as binary outputs,
high sparsity, and biological plausibility. However, the lack of effective
learning algorithms remains a challenge for SNNs. For instance, while
converting artificial neural networks (ANNs) to SNNs circumvents the need for
direct training of SNNs, it encounters issues related to conversion errors and
high inference time delays. In order to reduce or even eliminate conversion
errors while decreasing inference time-steps, we have introduced a novel type
of neuron called Group Neurons (GNs). One GN is composed of multiple
Integrate-and-Fire (IF) neurons as members, and its neural dynamics are
meticulously designed. Based on GNs, we have optimized the traditional ANN-SNN
conversion framework. Specifically, we replace the IF neurons in the SNNs
obtained by the traditional conversion framework with GNs. The resulting SNNs,
which utilize GNs, are capable of achieving accuracy levels comparable to ANNs
even within extremely short inference time-steps. The experiments on CIFAR10,
CIFAR100, and ImageNet datasets demonstrate the superiority of the proposed
methods in terms of both inference accuracy and latency. Code is available at
https://github.com/Lyu6PosHao/ANN2SNN_GN.
