A comprehensive survey on graph neural network accelerators

Deep learning has gained superior accuracy on Euclidean structure data in neural networks. As a result, non-Euclidean structure data, such as graph data, has more sophisticated structural information, which can be applied in neural networks as well to address more complex and practical problems. However, actual graph data obeys a power-law distribution, so the adjacent matrix of a graph is random and sparse. Graph processing accelerator (GPA) is designed to handle the problems above. However, graph computing only processes 1-dimensional data. In graph neural networks (GNNs), graph data is multi-dimensional. Consequently, GNNs include the execution processes of both traditional graph processing and neural network, which have irregular memory access and regular computation, respectively. To obtain more information in graph data and require better model generalization ability, the layers of GNN are deeper, so the overhead of memory access and computation is considerable. At present, GNN accelerators are designed to deal with this issue. In this paper, we conduct a systematic survey regarding the design and implementation of GNN accelerators. Specifically, we review the challenges faced by GNN accelerators, and existing related works in detail to process them. Finally, we evaluate previous works and propose future directions in this booming field.