  Recently, Graph Neural Networks (GNNs) have received a lot of interest
because of their success in learning representations from graph structured
data. However, GNNs exhibit different compute and memory characteristics
compared to traditional Deep Neural Networks (DNNs). Graph convolutions require
feature aggregations from neighboring nodes (known as the aggregation phase),
which leads to highly irregular data accesses. GNNs also have a very regular
compute phase that can be broken down to matrix multiplications (known as the
combination phase). All recently proposed GNN accelerators utilize different
dataflows and microarchitecture optimizations for these two phases. Different
communication strategies between the two phases have been also used. However,
as more custom GNN accelerators are proposed, the harder it is to qualitatively
classify them and quantitatively contrast them. In this work, we present a
taxonomy to describe several diverse dataflows for running GNN inference on
accelerators. This provides a structured way to describe and compare the
design-space of GNN accelerators.
