  Graph neural networks (GNN) represent an emerging line of deep learning
models that operate on graph structures. It is becoming more and more popular
due to its high accuracy achieved in many graph-related tasks. However, GNN is
not as well understood in the system and architecture community as its
counterparts such as multi-layer perceptrons and convolutional neural networks.
This work tries to introduce the GNN to our community. In contrast to prior
work that only presents characterizations of GCNs, our work covers a large
portion of the varieties for GNN workloads based on a general GNN description
framework. By constructing the models on top of two widely-used libraries, we
characterize the GNN computation at inference stage concerning general-purpose
and application-specific architectures and hope our work can foster more system
and architecture research for GNNs.
