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

Many real-world data can be represented as graphs with nodes denoting a collection of entities and edges denoting their pairwise relationships, such as individuals in social networks, financial transactions between firms and banks, atoms and bonds in molecules, and vehicles in transportation systems. Graph neural networks (GNNs) [45, 71, 125] have recently become the most widely used graph machine learning (ML) model for learning knowledge and making predictions on graph data. GNNs have achieved state-of-the-art performance in many graph ML applications. They are used, for example, in recommendations on social graphs [89, 136, 165], fraud account detection on financial graphs [31], drug discoveries from molecule graphs [64], traffic forecasting on transportation graphs [65], and so on. The superior performance of GNNs on graphs is mainly due to their ability to combine the entity information, represented as the node features, and the relationships, represented as the graph structure.