Spiking neural network (SNN) operating with asynchronous discrete events
shows higher energy efficiency. A popular approach to implementing deep SNNs is
ANN-SNN conversion combining both efficient training of ANNs and efficient
inference of SNNs. However, due to the intrinsic difference between ANNs and
SNNs, the accuracy loss is usually non-negligible, especially under low
simulating steps. It restricts the applications of SNN on latency-sensitive
edge devices greatly. In this paper, we identify such performance degradation
stems from the misrepresentation of the negative or overflow residual membrane
potential in SNNs. Inspired by this, we systematically analyze the conversion
error between SNNs and ANNs, and then decompose it into three folds:
quantization error, clipping error, and residual membrane potential
representation error. With such insights, we propose a dual-phase conversion
algorithm to minimize those errors separately. Besides, we show each phase
achieves significant performance gains in a complementary manner. We evaluate
our method on challenging datasets including CIFAR-10, CIFAR-100, and ImageNet
datasets. The experimental results show the proposed method achieves the
state-of-the-art in terms of both accuracy and latency with promising energy
preservation compared to ANNs. For instance, our method achieves an accuracy of
73.20% on CIFAR-100 in only 2 time steps with 15.7$\times$ less energy
consumption.

本论文提出一种双相转换算法，通过减少量化误差、裁剪误差和残余膜电势表示误差来提高深度脉冲神经网络（SNN）的准确性，并在 CIFAR-10、CIFAR-100 和 ImageNet 数据集上表现出与人工神经网络（ANNs）相当的准确性和更低的能源消耗。