Graph Neural Networks (GNNs) have emerged as a powerful and flexible
framework for representation learning on irregular data. As they generalize the
operations of classical CNNs on grids to arbitrary topologies, GNNs also bring
much of the implementation challenges of their Euclidean counterparts. Model
size, memory footprint, and energy consumption are common concerns for many
real-world applications. Network binarization allocates a single bit to
parameters and activations, thus dramatically reducing the memory requirements
(up to 32x compared to single-precision floating-point numbers) and maximizing
the benefits of fast SIMD instructions on modern hardware for measurable
speedups. However, in spite of the large body of work on binarization for
classical CNNs, this area remains largely unexplored in geometric deep
learning. In this paper, we present and evaluate different strategies for the
binarization of graph neural networks. We show that through careful design of
the models, and control of the training process, binary graph neural networks
can be trained at only a moderate cost in accuracy on challenging benchmarks.
In particular, we present the first dynamic graph neural network in Hamming
space, able to leverage efficient k-NN search on binary vectors to speed-up the
construction of the dynamic graph. We further verify that the binary models
offer significant savings on embedded devices. Our code is publicly available
on Github.

本论文探讨了将二值化技术应用于图神经网络的方法，并成功设计了能够在 Hamming 空间内快速构建动态图的模型，实现了在准确性轻微损失的代价下在嵌入式设备上取得了显著的存储空间和时间扩展的效果。

二元图神经网络

Binary Graph Neural Networks

To alleviate the resource constraint for real-time point cloud applications
that run on edge devices, in this paper we present BiPointNet, the first model
binarization approach for efficient deep learning on point clouds. We discover
that the immense performance drop of binarized models for point clouds mainly
stems from two challenges: aggregation-induced feature homogenization that
leads to a degradation of information entropy, and scale distortion that
hinders optimization and invalidates scale-sensitive structures. With
theoretical justifications and in-depth analysis, our BiPointNet introduces
Entropy-Maximizing Aggregation (EMA) to modulate the distribution before
aggregation for the maximum information entropy, and Layer-wise Scale Recovery
(LSR) to efficiently restore feature representation capacity. Extensive
experiments show that BiPointNet outperforms existing binarization methods by
convincing margins, at the level even comparable with the full precision
counterpart. We highlight that our techniques are generic, guaranteeing
significant improvements on various fundamental tasks and mainstream backbones.
Moreover, BiPointNet gives an impressive 14.7x speedup and 18.9x storage saving
on real-world resource-constrained devices.

本文提出了一种针对点云运算的模型二值化方法 BiPointNet，该方法引入了 Entropy-Maximizing Aggregation 和 Layer-wise Scale Recovery 来解决聚合引起的特征同质化和尺度失真问题，并在多个基本任务和主流骨干网络上实现了显著的改善，且在实际应用中取得了显著的性能提升效果，包括 14.7 倍的加速和 18.9 倍的存储节省。