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。

双相优化下实现超低延迟无损 ANN-SNN 转换

Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with  Dual-Phase Optimization

We introduce a Power-of-Two low-bit post-training quantization(PTQ) method
for deep neural network that meets hardware requirements and does not call for
long-time retraining. Power-of-Two quantization can convert the multiplication
introduced by quantization and dequantization to bit-shift that is adopted by
many efficient accelerators. However, the Power-of-Two scale factors have fewer
candidate values, which leads to more rounding or clipping errors. We propose a
novel Power-of-Two PTQ framework, dubbed RAPQ, which dynamically adjusts the
Power-of-Two scales of the whole network instead of statically determining them
layer by layer. It can theoretically trade off the rounding error and clipping
error of the whole network. Meanwhile, the reconstruction method in RAPQ is
based on the BN information of every unit. Extensive experiments on ImageNet
prove the excellent performance of our proposed method. Without bells and
whistles, RAPQ can reach accuracy of 65% and 48% on ResNet-18 and MobileNetV2
respectively with weight INT2 activation INT4. We are the first to propose the
more constrained but hardware-friendly Power-of-Two quantization scheme for
low-bit PTQ specially and prove that it can achieve nearly the same accuracy as
SOTA PTQ method. The code was released.

提出一种名为 RAPQ 的 Power-of-Two 低位后训练量化方法，动态调整整个网络的二的幂次方比例因子，用 BN 信息进行重构，证明该方法可以实现近似于 SOTA 方法的高精度，代码已发布。