mlsanityData to 14 Oct 2026

Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Vision

arXiv 2401.04988 ↗First posted 10 Jan 2024cs.CV eess.IVcited by 5

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Abstract (arXiv)

Event-based vision is an emerging research field involving processing data generated by Dynamic Vision Sensors (neuromorphic cameras). One of the latest proposals in this area are Graph Convolutional Networks (GCNs), which allow to process events in its original sparse form while maintaining high detection and classification performance. In this paper, we present the hardware implementation of a~graph generation process from an event camera data stream, taking into account both the advantages and limitations of FPGAs. We propose various ways to simplify the graph representation and use scaling and quantisation of values. We consider both undirected and directed graphs that enable the use of PointNet convolution. The results obtained show that by appropriately modifying the graph representation, it is possible to create a~hardware module for graph generation. Moreover, the proposed modifications have no significant impact on object detection performance, only 0.08% mAP less for the base model and the N-Caltech data set.Finally, we describe the proposed hardware architecture of the graph generation module.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 11 of its 12 references that are in the map. First posted on arXiv 10 Jan 2024.

Builds on

11 of 12 references.

References · 11

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2022AEGNN: Asynchronous Event-based Graph Neural Networkscited by 118
  3. 2019Graph-Based Object Classification for Neuromorphic Vision Sensingcited by 85
  4. 2022Object Detection with Spiking Neural Networks on Automotive Event Datacited by 80
  5. 2018EventNet: Asynchronous Recursive Event Processingcited by 58
  6. 2023Graph-based Asynchronous Event Processing for Rapid Object Recognitioncited by 51
  7. 2022Pushing the Limits of Asynchronous Graph-based Object Detection with Event Camerascited by 32
  8. 2023Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Camerascited by 11
  9. 2022GenGNN: A Generic FPGA Framework for Graph Neural Network Accelerationcited by 9
  10. 2021LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Acceleratorcited by 3
  11. 2023Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physicscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.

  1. 2024Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Datasetcited by 25
  2. 2024Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAscited by 7
  3. 2024Event-based vision on FPGAs -- a surveycited by 7
  4. 2025EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perceptioncited by 1
  5. 2026Neuromorphic Object Detection: An In-Depth Study and Future Directionscited by 0