mlsanityData to 7 Oct 2026

Event-based vision on FPGAs -- a survey

arXiv 2407.08356 ↗First posted 11 Jul 2024cs.CVcited by 7

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Abstract (arXiv)

In recent years there has been a growing interest in event cameras, i.e. vision sensors that record changes in illumination independently for each pixel. This type of operation ensures that acquisition is possible in very adverse lighting conditions, both in low light and high dynamic range, and reduces average power consumption. In addition, the independent operation of each pixel results in low latency, which is desirable for robotic solutions. Nowadays, Field Programmable Gate Arrays (FPGAs), along with general-purpose processors (GPPs/CPUs) and programmable graphics processing units (GPUs), are popular architectures for implementing and accelerating computing tasks. In particular, their usefulness in the embedded vision domain has been repeatedly demonstrated over the past 30 years, where they have enabled fast data processing (even in real-time) and energy efficiency. Hence, the combination of event cameras and reconfigurable devices seems to be a good solution, especially in the context of energy-efficient real-time embedded systems. This paper gives an overview of the most important works, where FPGAs have been used in different contexts to process event data. It covers applications in the following areas: filtering, stereovision, optical flow, acceleration of AI-based algorithms (including spiking neural networks) for object classification, detection and tracking, and applications in robotics and inspection systems. Current trends and challenges for such systems are also discussed.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 18 of its 80 references that are in the map. First posted on arXiv 11 Jul 2024.

Builds on

18 of 80 references.

References · 18

  1. 2019Event-based Vision: A Surveycited by 1,010
  2. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 308
  3. 2017NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Mapscited by 39
  4. 2019FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Reviewcited by 29
  5. 2017Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementationcited by 12
  6. 2023Within-Camera Multilayer Perceptron DVS Denoisingcited by 8
  7. 2024A Composable Dynamic Sparse Dataflow Architecture for Efficient Event-based Vision Processing on FPGAcited by 8
  8. 2021Event Camera Simulator Design for Modeling Attention-based Inference Architecturescited by 6
  9. 2019Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classificationcited by 5
  10. 2020Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detectioncited by 5
  11. 2024Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Visioncited by 5
  12. 2020A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-time Systemscited by 4
6 more
  1. 2022Hardware architecture for high throughput event visual data filtering with matrix of IIR filters algorithmcited by 4
  2. 2022Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platformcited by 3
  3. 2019PCA-RECT: An Energy-efficient Object Detection Approach for Event Camerascited by 2
  4. 2022Fusing Frame and Event Vision for High-speed Optical Flow for Edge Applicationcited by 2
  5. 2022FPGA Implementation of An Event-driven Saliency-based Selective Attention Modelcited by 2
  6. 2023High-definition event frame generation using SoC FPGA devicescited by 2

Built on by

Cited by year (2026 to 7 Oct)

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

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. 2025Distance Estimation in Outdoor Driving Environments Using Phase-only Correlation Method with Event Camerascited by 0
  5. 2025HOMI: Ultra-Fast EdgeAI platform for Event Camerascited by 0
  6. 2026EventShiftFlow: Towards Hardware-efficient FPGA-based Flow Estimationcited by 0
  7. 2026Hardware-aware Graph Neural Networks prunning for embedded event-based visioncited by 0