# GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks

arXiv: https://arxiv.org/abs/2103.10836 · HTML: https://mlsanity.com/paper/2103.10836/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment](https://mlsanity.com/paper/2307.08065/) (2023)
- [GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing](https://mlsanity.com/paper/2111.00680/) (2021)
- [GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching](https://mlsanity.com/paper/2105.10554/) (2021)
- [PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures](https://mlsanity.com/paper/2402.16731/) (2024)

## Abstract (arXiv)

Graph Neural Networks (GNNs) use a fully-connected layer to extract features from the nodes of a graph and aggregate these features using message passing between nodes, combining two distinct computational patterns: dense, regular computations and sparse, irregular computations. To address this challenge, we propose GNNerator, an accelerator with heterogeneous compute engines optimized for these two patterns. Further, GNNerator implements feature-blocking, a novel GNN dataflow that beneficially trades off irregular memory accesses during aggregation for regular memory accesses during feature extraction. We show GNNerator achieves speedups of 5.7-37x over an NVIDIA RTX 2080-Ti, and 2.3x-3.8x over HyGCN, a state-of-the-art GNN accelerator.