Graph Convolutional Networks (GCNs) achieve an impressive performance due to
the remarkable representation ability in learning the graph information.
However, GCNs, when implemented on a deep network, require expensive
computation power, making them difficult to be deployed on battery-powered
devices. In contrast, Spiking Neural Networks (SNNs), which perform a
bio-fidelity inference process, offer an energy-efficient neural architecture.
In this work, we propose SpikingGCN, an end-to-end framework that aims to
integrate the embedding of GCNs with the biofidelity characteristics of SNNs.
The original graph data are encoded into spike trains based on the
incorporation of graph convolution. We further model biological information
processing by utilizing a fully connected layer combined with neuron nodes. In
a wide range of scenarios (e.g. citation networks, image graph classification,
and recommender systems), our experimental results show that the proposed
method could gain competitive performance against state-of-the-art approaches.
Furthermore, we show that SpikingGCN on a neuromorphic chip can bring a clear
advantage of energy efficiency into graph data analysis, which demonstrates its
great potential to construct environment-friendly machine learning models.

本文提出了 SpikingGCN，一个将 GCN 的嵌入与 SNN 的生物保真特性整合在一起的端到端框架，实现了在众多场景中能够达到竞争水平的性能，同时在神经形态芯片上实现了能效的优势，展示了其建立环境友好的机器学习模型的巨大潜力。

脉冲图卷积网络

Spiking Graph Convolutional Networks

Over the past decade, deep neural networks (DNNs) have demonstrated
remarkable performance in a variety of applications. As we try to solve more
advanced problems, increasing demands for computing and power resources has
become inevitable. Spiking neural networks (SNNs) have attracted widespread
interest as the third-generation of neural networks due to their event-driven
and low-powered nature. SNNs, however, are difficult to train, mainly owing to
their complex dynamics of neurons and non-differentiable spike operations.
Furthermore, their applications have been limited to relatively simple tasks
such as image classification. In this study, we investigate the performance
degradation of SNNs in a more challenging regression problem (i.e., object
detection). Through our in-depth analysis, we introduce two novel methods:
channel-wise normalization and signed neuron with imbalanced threshold, both of
which provide fast and accurate information transmission for deep SNNs.
Consequently, we present a first spiked-based object detection model, called
Spiking-YOLO. Our experiments show that Spiking-YOLO achieves remarkable
results that are comparable (up to 98%) to those of Tiny YOLO on non-trivial
datasets, PASCAL VOC and MS COCO. Furthermore, Spiking-YOLO on a neuromorphic
chip consumes approximately 280 times less energy than Tiny YOLO and converges
2.3 to 4 times faster than previous SNN conversion methods.

本研究旨在研究脉冲神经网络在目标检测中的性能衰退，并提出了两种新方法：逐通道归一化和具有不平衡阈值的有符号神经元，以提高深度 SNN 的信息传输速度和准确性。最终实现了基于脉冲的目标检测模型，Spiking-YOLO，其结果表现可与 Tiny YOLO 比肩，并且在神经形态芯片上的能耗极低，且比以前的 SNN 转换方法更快。