# Event-based vision on FPGAs -- a survey

arXiv: https://arxiv.org/abs/2407.08356 · HTML: https://mlsanity.com/paper/2407.08356/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 80 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps](https://mlsanity.com/paper/1706.01406/) (2017)
- [FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Review](https://mlsanity.com/paper/1901.00121/) (2019)
- [Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation](https://mlsanity.com/paper/1706.05415/) (2017)
- [Within-Camera Multilayer Perceptron DVS Denoising](https://mlsanity.com/paper/2304.07543/) (2023)
- [A Composable Dynamic Sparse Dataflow Architecture for Efficient Event-based Vision Processing on FPGA](https://mlsanity.com/paper/2401.05626/) (2024)
- [Event Camera Simulator Design for Modeling Attention-based Inference Architectures](https://mlsanity.com/paper/2105.01203/) (2021)
- [Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification](https://mlsanity.com/paper/1905.07419/) (2019)
- [Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection](https://mlsanity.com/paper/2004.01689/) (2020)
- [Optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-based Vision](https://mlsanity.com/paper/2401.04988/) (2024)
- [A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-time Systems](https://mlsanity.com/paper/2007.11404/) (2020)
- [Hardware architecture for high throughput event visual data filtering with matrix of IIR filters algorithm](https://mlsanity.com/paper/2207.00860/) (2022)
- [Eventor: An Efficient Event-Based Monocular Multi-View Stereo Accelerator on FPGA Platform](https://mlsanity.com/paper/2203.15439/) (2022)
- [PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras](https://mlsanity.com/paper/1904.12665/) (2019)
- [Fusing Frame and Event Vision for High-speed Optical Flow for Edge Application](https://mlsanity.com/paper/2207.10720/) (2022)
- [FPGA Implementation of An Event-driven Saliency-based Selective Attention Model](https://mlsanity.com/paper/2211.14060/) (2022)
- [High-definition event frame generation using SoC FPGA devices](https://mlsanity.com/paper/2307.14177/) (2023)

## Built on by (most cited first, 7 of 7)

- [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)
- [Distance Estimation in Outdoor Driving Environments Using Phase-only Correlation Method with Event Cameras](https://mlsanity.com/paper/2505.17582/) (2025)
- [HOMI: Ultra-Fast EdgeAI platform for Event Cameras](https://mlsanity.com/paper/2508.12637/) (2025)
- [EventShiftFlow: Towards Hardware-efficient FPGA-based Flow Estimation](https://mlsanity.com/paper/2605.28312/) (2026)
- [Hardware-aware Graph Neural Networks prunning for embedded event-based vision](https://mlsanity.com/paper/2607.06739/) (2026)

## 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.