  We present GRIP, a graph neural network accelerator architecture designed for
low-latency inference. AcceleratingGNNs is challenging because they combine two
distinct types of computation: arithmetic-intensive vertex-centric operations
and memory-intensive edge-centric operations. GRIP splits GNN inference into a
fixed set of edge- and vertex-centric execution phases that can be implemented
in hardware. We then specialize each unit for the unique computational
structure found in each phase.For vertex-centric phases, GRIP uses a high
performance matrix multiply engine coupled with a dedicated memory subsystem
for weights to improve reuse. For edge-centric phases, GRIP use multiple
parallel prefetch and reduction engines to alleviate the irregularity in memory
accesses. Finally, GRIP supports severalGNN optimizations, including a novel
optimization called vertex-tiling which increases the reuse of weight data.We
evaluate GRIP by performing synthesis and place and route for a 28nm
implementation capable of executing inference for several widely-used GNN
models (GCN, GraphSAGE, G-GCN, and GIN). Across several benchmark graphs, it
reduces 99th percentile latency by a geometric mean of 17x and 23x compared to
a CPU and GPU baseline, respectively, while drawing only 5W.
