Group IF Units with Membrane Potential Sharing for High-Accuracy Low-Latency Spiking Neural Networks
Abstract
Spiking neural networks (SNNs) have attracted much attention due to their low energy consumption and fast inference on neuromorphic hardware. Currently, the most effective way to implement deep SNNs is through ANN-SNN conversion, which combines the maturity of ANNs with the fast inference of SNNs, achieving accuracy comparable to ANNs on large-scale datasets. However, to achieve this accuracy, a large number of time steps are usually required, which undermines the low energy consumption and fast inference advantages of SNNs. When the number of time steps is very limited, the converted SNNs face a severe drop in accuracy, especially in the case of a single time step, which severely restricts the practical application of SNNs. In this paper, we analyze the main reasons why ANN- SNN cannot achieve lossless conversion with very limited number of time steps, and then proposes a group IF units with shared membrane potential to achieve lossless conversion of ANN-SNN at extremely low latency. We validate the effectiveness of our method on CIFAR-10, CIFAR-100, and ImageNet. Experiments show that our proposed method outperforms the existing state- of-the-art methods at the same number of time steps. Moreover, we have achieved an nearly lossless conversion of ANN-SNN in a single time step to get a high-accuracy SNN for the first time. For example, for VGG-16, our method only loses 0.8% and 3.15% of the accuracy compared to ANNs on CIFAR-10/100 in a single time step.
Authors: Zhenxiong Ye, Weiming Zeng, Yunhua Chen, Ling Zhang, Jinsheng Xiao, Irwin King