GRIP: A Graph Neural Network Accelerator Architecture

TL;DR

GRIP is a specialized hardware architecture for graph neural network inference that significantly reduces latency by optimizing vertex- and edge-centric computations, enabling efficient low-power GNN processing.

Contribution

The paper introduces GRIP, a novel GNN accelerator architecture with fixed execution phases and specialized units, including a vertex-tiling optimization, for improved performance.

Findings

Achieves 17x latency reduction over CPU

Achieves 23x latency reduction over GPU

Operates at 5W power consumption

Abstract

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.