  Graph Neural Networks (GNNs) have exploded onto the machine learning scene in
recent years owing to their capability to model and learn from graph-structured
data. Such an ability has strong implications in a wide variety of fields whose
data is inherently relational, for which conventional neural networks do not
perform well. Indeed, as recent reviews can attest, research in the area of
GNNs has grown rapidly and has lead to the development of a variety of GNN
algorithm variants as well as to the exploration of groundbreaking applications
in chemistry, neurology, electronics, or communication networks, among others.
At the current stage of research, however, the efficient processing of GNNs is
still an open challenge for several reasons. Besides of their novelty, GNNs are
hard to compute due to their dependence on the input graph, their combination
of dense and very sparse operations, or the need to scale to huge graphs in
some applications. In this context, this paper aims to make two main
contributions. On the one hand, a review of the field of GNNs is presented from
the perspective of computing. This includes a brief tutorial on the GNN
fundamentals, an overview of the evolution of the field in the last decade, and
a summary of operations carried out in the multiple phases of different GNN
algorithm variants. On the other hand, an in-depth analysis of current software
and hardware acceleration schemes is provided, from which a hardware-software,
graph-aware, and communication-centric vision for GNN accelerators is
distilled.
