# GRIP: A Graph Neural Network Accelerator Architecture

arXiv: https://arxiv.org/abs/2007.13828 · HTML: https://mlsanity.com/paper/2007.13828/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 30 of 37)

- [A Unified Lottery Ticket Hypothesis for Graph Neural Networks](https://mlsanity.com/paper/2102.06790/) (2021)
- [Sampling methods for efficient training of graph convolutional networks: A survey](https://mlsanity.com/paper/2103.05872/) (2021)
- [FLAT: An Optimized Dataflow for Mitigating Attention Bottlenecks](https://mlsanity.com/paper/2107.06419/) (2021)
- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization](https://mlsanity.com/paper/2203.03606/) (2022)
- [Graph Neural Networks for Charged Particle Tracking on FPGAs](https://mlsanity.com/paper/2112.02048/) (2021)
- [G-CoS: GNN-Accelerator Co-Search Towards Both Better Accuracy and Efficiency](https://mlsanity.com/paper/2109.08983/) (2021)
- [The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey](https://mlsanity.com/paper/2305.13854/) (2023)
- [GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design](https://mlsanity.com/paper/2112.11594/) (2021)
- [SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures](https://mlsanity.com/paper/2205.04711/) (2022)
- [Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges](https://mlsanity.com/paper/2203.12852/) (2022)
- [Neural Graph Databases](https://mlsanity.com/paper/2209.09732/) (2022)
- [HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers](https://mlsanity.com/paper/2311.18526/) (2023)
- [GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration](https://mlsanity.com/paper/2201.08475/) (2022)
- [MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment](https://mlsanity.com/paper/2307.08065/) (2023)
- [GHOST: A Graph Neural Network Accelerator using Silicon Photonics](https://mlsanity.com/paper/2307.01782/) (2023)
- [Fully Unified Motion Planning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2504.12667/) (2025)
- [GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks](https://mlsanity.com/paper/2203.00158/) (2022)
- [Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach](https://mlsanity.com/paper/2303.00524/) (2023)
- [Graph Neural Networks on Quantum Computers](https://mlsanity.com/paper/2405.17060/) (2024)
- [PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures](https://mlsanity.com/paper/2402.16731/) (2024)
- [HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning](https://mlsanity.com/paper/2403.05763/) (2024)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Convolutions for Spatial Interaction Modeling](https://mlsanity.com/paper/2104.07182/) (2021)
- [PyGFI: Analyzing and Enhancing Robustness of Graph Neural Networks Against Hardware Errors](https://mlsanity.com/paper/2212.03475/) (2022)
- [Slice-and-Forge: Making Better Use of Caches for Graph Convolutional Network Accelerators](https://mlsanity.com/paper/2301.09813/) (2023)
- [GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization](https://mlsanity.com/paper/2303.16459/) (2023)
- [Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs](https://mlsanity.com/paper/2306.11330/) (2023)
- [STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphs](https://mlsanity.com/paper/2309.15875/) (2023)
- [STRIDE: Structure and Embedding Distillation with Attention for Graph Neural Networks](https://mlsanity.com/paper/2310.15938/) (2023)

## Abstract (arXiv)

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.