Spiking Neural Networks (SNNs) have received extensive academic attention due
to the unique properties of low power consumption and high-speed computing on
neuromorphic chips. Among various training methods of SNNs, ANN-SNN conversion
has shown the equivalent level of performance as ANNs on large-scale datasets.
However, unevenness error, which refers to the deviation caused by different
temporal sequences of spike arrival on activation layers, has not been
effectively resolved and seriously suffers the performance of SNNs under the
condition of short time-steps. In this paper, we make a detailed analysis of
unevenness error and divide it into four categories. We point out that the case
of the ANN output being zero while the SNN output being larger than zero
accounts for the largest percentage. Based on this, we theoretically prove the
sufficient and necessary conditions of this case and propose an optimization
strategy based on residual membrane potential to reduce unevenness error. The
experimental results show that the proposed method achieves state-of-the-art
performance on CIFAR-10, CIFAR-100, and ImageNet datasets. For example, we
reach top-1 accuracy of 64.32\% on ImageNet with 10-steps. To the best of our
knowledge, this is the first time ANN-SNN conversion can simultaneously achieve
high accuracy and ultra-low-latency on the complex dataset. Code is available
at this https URL\_SRP.

本文提出了一种基于剩余膜电位的优化策略，旨在降低脉冲到达时间序列下 SNNs 中不平衡误差，从而达到与 ANNs 等效水平的性能，实验结果表明该策略在复杂数据集 ImageNet 上取得了最先进的性能，例如在 10 步之内达到了 64.32% 的准确率。

通过剩余膜电位减少 ANN-SNN 转换误差

Reducing ANN-SNN Conversion Error through Residual Membrane Potential

Spiking Neural Networks (SNNs) have recently attracted significant research
interest as the third generation of artificial neural networks that can enable
low-power event-driven data analytics. The best performing SNNs for image
recognition tasks are obtained by converting a trained Analog Neural Network
(ANN), consisting of Rectified Linear Units (ReLU), to SNN composed of
integrate-and-fire neurons with "proper" firing thresholds. The converted SNNs
typically incur loss in accuracy compared to that provided by the original ANN
and require sizable number of inference time-steps to achieve the best
accuracy. We find that performance degradation in the converted SNN stems from
using "hard reset" spiking neuron that is driven to fixed reset potential once
its membrane potential exceeds the firing threshold, leading to information
loss during SNN inference. We propose ANN-SNN conversion using "soft reset"
spiking neuron model, referred to as Residual Membrane Potential (RMP) spiking
neuron, which retains the "residual" membrane potential above threshold at the
firing instants. We demonstrate near loss-less ANN-SNN conversion using RMP
neurons for VGG-16, ResNet-20, and ResNet-34 SNNs on challenging datasets
including CIFAR-10 (93.63% top-1), CIFAR-100 (70.93% top-1), and ImageNet
(73.09% top-1 accuracy). Our results also show that RMP-SNN surpasses the best
inference accuracy provided by the converted SNN with "hard reset" spiking
neurons using 2-8 times fewer inference time-steps across network architectures
and datasets.

使用 Residual Membrane Potential (RMP) 神经元的 ANN-SNN 转换可在多个数据集和网络结构上实现近乎零损失的图像识别和推理，且优于使用硬复位尖峰神经元的 SNN 推理精度。