A Survey on Graph Neural Network Acceleration:A Hardware Perspective

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 incompati-bility 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 re-cent 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.