Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient
computing compared with Artificial Neural Networks (ANNs), closely mirroring
biological neural processes. However, this potential comes with inherent
challenges in directly training SNNs through spatio-temporal backpropagation --
stemming from the temporal dynamics of spiking neurons and their discrete
signal processing -- which necessitates alternative ways of training, most
notably through ANN-SNN conversion. In this work, we introduce a lightweight
Forward Temporal Bias Correction (FTBC) technique, aimed at enhancing
conversion accuracy without the computational overhead. We ground our method on
provided theoretical findings that through proper temporal bias calibration the
expected error of ANN-SNN conversion can be reduced to be zero after each time
step. We further propose a heuristic algorithm for finding the temporal bias
only in the forward pass, thus eliminating the computational burden of
backpropagation and we evaluate our method on CIFAR-10/100 and ImageNet
datasets, achieving a notable increase in accuracy on all datasets. Codes are
released at a GitHub repository.

通过引入轻量级的 Forward Temporal Bias Correction (FTBC) 技术，我们提出了一种改善 ANN-SNN 转换准确性的方法，通过适当的时间偏差校准，使得在每个时间步长后 ANN-SNN 转换的预期误差减少为零。我们进一步提出了一种启发式算法，在前向传递中仅查找时间偏差，从而消除了反向传播的计算负担，并在 CIFAR-10/100 和 ImageNet 数据集上评估了我们的方法，在所有数据集上实现了显著的准确性增加。

FTBC：用于优化 ANN-SNN 转换的前向时间偏差校正

FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion

In the past years, artificial neural networks (ANNs) have become the de-facto
standard to solve tasks in communications engineering that are difficult to
solve with traditional methods. In parallel, the artificial intelligence
community drives its research to biology-inspired, brain-like spiking neural
networks (SNNs), which promise extremely energy-efficient computing. In this
paper, we investigate the use of SNNs in the context of channel equalization
for ultra-low complexity receivers. We propose an SNN-based equalizer with a
feedback structure akin to the decision feedback equalizer (DFE). For
conversion of real-world data into spike signals we introduce a novel ternary
encoding and compare it with traditional log-scale encoding. We show that our
approach clearly outperforms conventional linear equalizers for three different
exemplary channels. We highlight that mainly the conversion of the channel
output to spikes introduces a small performance penalty. The proposed SNN with
a decision feedback structure enables the path to competitive energy-efficient
transceivers.

本文在通信工程中对脑神经元类似的尖峰神经网络（SNN）进行了研究，提出了一种类似于决策反馈均衡器（DFE）的基于 SNN 的均衡器，并展示了其相比于传统的线性均衡器在三个典型信道上明显的优化效果，为实现低功耗的通信系统提供了可行方案。