GRIP: A Graph Neural Network Accelerator Architecture
Kevin Kiningham, Philip Levis, and Christopher Re
Published in IEEE TRANSACTIONS ON COMPUTERS, September 2022.
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 nm 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 5 W.
Paper (1MB)
BibTeX entry
@inproceedings{grip-ieeecomputer22, author = "Kevin Kiningham and Philip Levis and Christopher Re", title = "{GRIP: A Graph Neural Network Accelerator Architecture}", booktitle = "{IEEE TRANSACTIONS ON COMPUTERS}", year = {2022}, month = {September} }