A Survey on Graph Neural Network Acceleration: A Hardware Perspective

Abstract

Graph neural networks (GNNs) have emerged as powerful approaches to learn knowledge about graphs and vertices. The rapid employment of GNNs poses requirements for processing efficiency. Due to incompatibility of general platforms, dedicated hardware devices and platforms are developed to efficiently accelerate training and inference of GNNs. We conduct a survey on hardware acceleration for GNNs. We first include and introduce recent advances of the domain, and then provide a methodology of categorization to classify existing works into three categories. Next, we discuss optimization techniques adopted at different levels. And finally we propose suggestions on future directions to facilitate further works.