mlsanityData to 14 Oct 2026

Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs

arXiv 2406.07318 ↗First posted 11 Jun 2024cs.CV cs.AR eess.IVcited by 7

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Abstract (arXiv)

The utilisation of event cameras represents an important and swiftly evolving trend aimed at addressing the constraints of traditional video systems. Particularly within the automotive domain, these cameras find significant relevance for their integration into embedded real-time systems due to lower latency and power consumption. One effective approach to ensure the necessary throughput and latency for event processing is through the utilisation of graph convolutional networks (GCNs). In this study, we introduce a custom EFGCN (Event-based FPGA-accelerated Graph Convolutional Network) designed with a series of hardware-aware optimisations tailored for PointNetConv,a graph convolution designed for point cloud processing. The proposed techniques result in up to 100-fold reduction in model size compared to Asynchronous Event-based GNN (AEGNN), one of the most recent works in the field, with a relatively small decrease in accuracy (2.9% for the N-Caltech101 classification task, 2.2% for the N-Cars classification task), thus following the TinyML trend. We implemented EFGCN on a ZCU104 SoC FPGA platform without any off-chip external memory resources, achieving a throughput of 13.3 million events per second (MEPS) and real-time partially asynchronous processing with low latency. Across multiple event-based classification benchmarks, our approach achieves competitive accuracy while providing state-of-the-art computational efficiency per event, small model size, and high scalability, customisability and resource efficiency. We publish both software and hardware source code in an open repository: https://github.com/vision-agh/gcnn-dvs-fpga.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 43 of its 79 references that are in the map. First posted on arXiv 11 Jun 2024.

Builds on

43 of 79 references.

References · 43

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31 more
  1. 2019End-to-End Learning of Representations for Asynchronous Event-Based Datacited by 217
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  18. 2021A Voxel Graph CNN for Object Classification with Event Camerascited by 38
  19. 2022Pushing the Limits of Asynchronous Graph-based Object Detection with Event Camerascited by 32
  20. 2021Learning from Event Cameras with Sparse Spiking Convolutional Neural Networkscited by 25
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  26. 2024A Composable Dynamic Sparse Dataflow Architecture for Efficient Event-based Vision Processing on FPGAcited by 8
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  28. 2024Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Visioncited by 5
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  31. 2022An Efficient Accelerator for Deep Learning-based Point Cloud Registration on FPGAscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 4.

7 papers.

  1. 2026Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGAcited by 2
  2. 2024Increasing the scalability of graph convolution for FPGA-implemented event-based visioncited by 1
  3. 2025Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGAcited by 1
  4. 2025EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perceptioncited by 1
  5. 2026Analog Quantum Asynchronous Event-Based Graph Neural Networkcited by 0
  6. 2026Hardware-aware Graph Neural Networks prunning for embedded event-based visioncited by 0
  7. 2026ETHEREAL: A 25.6-μs/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edgecited by 0