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We present GRIP, a graph neural network accelerator architecture designed for low-latency inference. Accelerating GNNs 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 three edge- and vertex-centric execution phases that can be implemented in hardware. GRIP specializes 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 several GNN optimizations, including an optimization called vertex-tiling that increases the reuse of weight data. We evaluate GRIP by performing synthesis and place and route for a   $28 \;\mathrm{n}\mathrm{m}$28n   mimplementation 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   $17\times$17   &#x00D7;and   $23\times$23   &#x00D7;compared to a CPU and GPU baseline, respectively, while drawing only   $5 \;\mathrm{W}$5W.

GRIP: A Graph Neural Network Accelerator Architecture