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Graph neural networks (GNNs) are emerging for machine learning research ongraph-structured data. GNNs achieve state-of-the-art performance on many tasks,but they face scalability challenges when it comes to real-world applicationsthat have numerous data and strict latency requirements. Many studies have beenconducted on how to accelerate GNNs in an effort to address these challenges.These acceleration techniques touch on various aspects of the GNN pipeline,from smart training and inference algorithms to efficient systems andcustomized hardware. As the amount of research on GNN acceleration has grownrapidly, there lacks a systematic treatment to provide a unified view andaddress the complexity of relevant works. In this survey, we provide a taxonomyof GNN acceleration, review the existing approaches, and suggest futureresearch directions. Our taxonomic treatment of GNN acceleration connects theexisting works and sets the stage for further development in this area.

A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware