# GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration

arXiv: https://arxiv.org/abs/2201.08475 · HTML: https://mlsanity.com/paper/2201.08475/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 40 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Builds on (most cited first, 23 of 23)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [Principal Neighbourhood Aggregation for Graph Nets](https://mlsanity.com/paper/2004.05718/) (2020)
- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://mlsanity.com/paper/2003.01251/) (2020)
- [Directional Graph Networks](https://mlsanity.com/paper/2010.02863/) (2020)
- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
- [GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2005.00406/) (2020)
- [Computing Graph Neural Networks: A Survey from Algorithms to Accelerators](https://mlsanity.com/paper/2010.00130/) (2020)
- [HyGCN: A GCN Accelerator with Hybrid Architecture](https://mlsanity.com/paper/2001.02514/) (2020)
- [EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks](https://mlsanity.com/paper/1909.00155/) (2019)
- [GRIP: A Graph Neural Network Accelerator Architecture](https://mlsanity.com/paper/2007.13828/) (2020)
- [Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics](https://mlsanity.com/paper/2008.03601/) (2020)
- [GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms](https://mlsanity.com/paper/2001.02498/) (2019)
- [Graph Classification via Deep Learning with Virtual Nodes](https://mlsanity.com/paper/1708.04357/) (2017)
- [Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis](https://mlsanity.com/paper/1902.01020/) (2019)
- [MELOPPR: Software/Hardware Co-design for Memory-efficient Low-latency Personalized PageRank](https://mlsanity.com/paper/2104.09616/) (2021)

## Built on by (most cited first, 9 of 9)

- [LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics](https://mlsanity.com/paper/2209.14065/) (2022)
- [Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Vision](https://mlsanity.com/paper/2401.04988/) (2024)
- [DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference](https://mlsanity.com/paper/2304.06831/) (2023)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [InTAR: Inter-Task Auto-Reconfigurable Accelerator Design for High Data Volume Variation in DNNs](https://mlsanity.com/paper/2502.08807/) (2025)
- [GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization](https://mlsanity.com/paper/2303.16459/) (2023)
- [Attention, Distillation, and Tabularization: Towards Practical Neural Network-Based Prefetching](https://mlsanity.com/paper/2401.06362/) (2023)
- [AMPLE: Event-Driven Accelerator for Mixed-Precision Inference of Graph Neural Networks](https://mlsanity.com/paper/2502.21196/) (2025)
- [GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis](https://mlsanity.com/paper/2309.16022/) (2023)

## Abstract (arXiv)

Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to ubiquitous graph-related problems such as quantum chemistry, drug discovery, and high energy physics. However, meeting demand for novel GNN models and fast inference simultaneously is challenging because of the gap between the difficulty in developing efficient FPGA accelerators and the rapid pace of creation of new GNN models. Prior art focuses on the acceleration of specific classes of GNNs but lacks the generality to work across existing models or to extend to new and emerging GNN models. In this work, we propose a generic GNN acceleration framework using High-Level Synthesis (HLS), named GenGNN, with two-fold goals. First, we aim to deliver ultra-fast GNN inference without any graph pre-processing for real-time requirements. Second, we aim to support a diverse set of GNN models with the extensibility to flexibly adapt to new models. The framework features an optimized message-passing structure applicable to all models, combined with a rich library of model-specific components. We verify our implementation on-board on the Xilinx Alveo U50 FPGA and observe a speed-up of up to 25x against CPU (6226R) baseline and 13x against GPU (A6000) baseline. Our HLS code will be open-source on GitHub upon acceptance.