  Graph neural networks (GNNs) are emerging for machine learning research on
graph-structured data. GNNs achieve state-of-the-art performance on many tasks,
but they face scalability challenges when it comes to real-world applications
that have numerous data and strict latency requirements. Many studies have been
conducted 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 and
customized hardware. As the amount of research on GNN acceleration has grown
rapidly, there lacks a systematic treatment to provide a unified view and
address the complexity of relevant works. In this survey, we provide a taxonomy
of GNN acceleration, review the existing approaches, and suggest future
research directions. Our taxonomic treatment of GNN acceleration connects the
existing works and sets the stage for further development in this area.
