  Graph neural networks (GNNs) have emerged as a powerful approach for
modelling and learning from graph-structured data. Multiple fields have since
benefitted enormously from the capabilities of GNNs, such as recommendation
systems, social network analysis, drug discovery, and robotics. However,
accelerating and efficiently processing GNNs require a unique approach that
goes beyond conventional artificial neural network accelerators, due to the
substantial computational and memory requirements of GNNs. The slowdown of
scaling in CMOS platforms also motivates a search for alternative
implementation substrates. In this paper, we present GHOST, the first
silicon-photonic hardware accelerator for GNNs. GHOST efficiently alleviates
the costs associated with both vertex-centric and edge-centric operations. It
implements separately the three main stages involved in running GNNs in the
optical domain, allowing it to be used for the inference of various widely used
GNN models and architectures, such as graph convolution networks and graph
attention networks. Our simulation studies indicate that GHOST exhibits at
least 10.2x better throughput and 3.8x better energy efficiency when compared
to GPU, TPU, CPU and multiple state-of-the-art GNN hardware accelerators.
