# EvGNN: An Event-driven Graph Neural Network Accelerator for Edge Vision

arXiv: https://arxiv.org/abs/2404.19489 · HTML: https://mlsanity.com/paper/2404.19489/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 70 references that are in the map.
- First posted on arXiv 30 Apr 2024.

## Builds on (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [Diffusion-Convolutional Neural Networks](https://mlsanity.com/paper/1511.02136/) (2015)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [End-to-End Learning of Representations for Asynchronous Event-Based Data](https://mlsanity.com/paper/1904.08245/) (2019)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels](https://mlsanity.com/paper/1711.08920/) (2017)
- [Adaptive Graph Convolutional Neural Networks](https://mlsanity.com/paper/1801.03226/) (2018)
- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [Event-based Asynchronous Sparse Convolutional Networks](https://mlsanity.com/paper/2003.09148/) (2020)
- [Graph Convolutional Networks with EigenPooling](https://mlsanity.com/paper/1904.13107/) (2019)
- [Molecular graph generation with Graph Neural Networks](https://mlsanity.com/paper/2012.07397/) (2020)
- [Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras](https://mlsanity.com/paper/1805.07931/) (2018)
- [Graph-based Asynchronous Event Processing for Rapid Object Recognition](https://mlsanity.com/paper/2308.14419/) (2023)
- [HFirst: A Temporal Approach to Object Recognition](https://mlsanity.com/paper/1508.01176/) (2015)
- [TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation](https://mlsanity.com/paper/2307.07813/) (2023)
- [Deep Positron: A Deep Neural Network Using the Posit Number System](https://mlsanity.com/paper/1812.01762/) (2018)
- [BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices](https://mlsanity.com/paper/2104.06214/) (2021)

## Built on by (most cited first, 13 of 13)

- [Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs](https://mlsanity.com/paper/2406.07318/) (2024)
- [GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing Units](https://mlsanity.com/paper/2502.06921/) (2025)
- [Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA](https://mlsanity.com/paper/2602.16442/) (2026)
- [Increasing the scalability of graph convolution for FPGA-implemented event-based vision](https://mlsanity.com/paper/2411.04269/) (2024)
- [Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA](https://mlsanity.com/paper/2503.06629/) (2025)
- [HOMI: Ultra-Fast EdgeAI platform for Event Cameras](https://mlsanity.com/paper/2508.12637/) (2025)
- [GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA](https://mlsanity.com/paper/2604.21290/) (2026)
- [Programmable Silicon Retina on Pixel Processor Array](https://mlsanity.com/paper/2606.08370/) (2026)
- [A Multimodal RGB and Events Dataset for Hand Detection in First-Person View](https://mlsanity.com/paper/2606.10790/) (2026)
- [Analog Quantum Asynchronous Event-Based Graph Neural Network](https://mlsanity.com/paper/2606.11000/) (2026)
- [Hardware-aware Graph Neural Networks prunning for embedded event-based vision](https://mlsanity.com/paper/2607.06739/) (2026)
- [ETHEREAL: A 25.6-μs/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edge](https://mlsanity.com/paper/2608.17787/) (2026)
- [Real-time Unsupervised Object Discovery from Asynchronous Event Streams](https://mlsanity.com/paper/2608.26644/) (2026)

## Abstract (arXiv)

Edge vision systems combining sensing and embedded processing promise low-latency, decentralized, and energy-efficient solutions that forgo reliance on the cloud. As opposed to conventional frame-based vision sensors, event-based cameras deliver a microsecond-scale temporal resolution with sparse information encoding, thereby outlining new opportunities for edge vision systems. However, mainstream algorithms for frame-based vision, which mostly rely on convolutional neural networks (CNNs), can hardly exploit the advantages of event-based vision as they are typically optimized for dense matrix-vector multiplications. While event-driven graph neural networks (GNNs) have recently emerged as a promising solution for sparse event-based vision, their irregular structure is a challenge that currently hinders the design of efficient hardware accelerators. In this paper, we propose EvGNN, the first event-driven GNN accelerator for low-footprint, ultra-low-latency, and high-accuracy edge vision with event-based cameras. It relies on three central ideas: (i) directed dynamic graphs exploiting single-hop nodes with edge-free storage, (ii) event queues for the efficient identification of local neighbors within a spatiotemporally decoupled search range, and (iii) a novel layer-parallel processing scheme allowing for a low-latency execution of multi-layer GNNs. We deployed EvGNN on a Xilinx KV260 Ultrascale+ MPSoC platform and benchmarked it on the N-CARS dataset for car recognition, demonstrating a classification accuracy of 87.8% and an average latency per event of 16μs, thereby enabling real-time, microsecond-resolution event-based vision at the edge.