Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

Abstract

Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urgent demand is unsurprisingly made to accelerate GNNs for more efficient execution. In this paper, we provide a comprehensive survey on acceleration methods for GNNs from an algorithmic perspective. We first present a new taxonomy to classify existing acceleration methods into five categories. Based on the classification, we systematically discuss these methods and highlight their correlations. Next, we provide comparisons from aspects of the efficiency and characteristics of these methods. Finally, we suggest some promising prospects for future research.

A Survey of Computationally Efficient Graph Neural Networks for Reconfigurable Systems

“…There are also general information on accelerators and efficient GNNs [29,58,59]. Liu et al [60] approach current and future GNN work from an algorithmic perspective, while Abadal et al [61] provide a comprehensive overview of the acceleration algorithms and GNN fundamentals. As shown in Figure 1, we review hardware-based accelerators and quantization approaches for computationally efficient GNNs, with a particular focus on energy-constrained embedded device applications.…”