# A Survey of FPGA-Based Robotic Computing

arXiv: https://arxiv.org/abs/2009.06034 · HTML: https://mlsanity.com/paper/2009.06034/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2020.

## Built on by (most cited first, 8 of 8)

- [Low Latency Visual Inertial Odometry with On-Sensor Accelerated Optical Flow for Resource-Constrained UAVs](https://mlsanity.com/paper/2406.13345/) (2024)
- [iELAS: An ELAS-Based Energy-Efficient Accelerator for Real-Time Stereo Matching on FPGA Platform](https://mlsanity.com/paper/2104.05112/) (2021)
- [An Energy-Efficient Quad-Camera Visual System for Autonomous Machines on FPGA Platform](https://mlsanity.com/paper/2104.00192/) (2021)
- [Learning Quantized Continuous Controllers for Integer Hardware](https://mlsanity.com/paper/2511.07046/) (2025)
- [AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs](https://mlsanity.com/paper/2102.02988/) (2021)
- [FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems](https://mlsanity.com/paper/2203.07276/) (2022)
- [Real-time FPGA implementation of the Semi-Global Matching stereo vision algorithm for a 4K/UHD video stream](https://mlsanity.com/paper/2301.04847/) (2023)
- [Deep Learning Perspective of Scene Understanding in Autonomous Robots](https://mlsanity.com/paper/2512.14020/) (2025)

## Abstract (arXiv)

Recent researches on robotics have shown significant improvement, spanning from algorithms, mechanics to hardware architectures. Robotics, including manipulators, legged robots, drones, and autonomous vehicles, are now widely applied in diverse scenarios. However, the high computation and data complexity of robotic algorithms pose great challenges to its applications. On the one hand, CPU platform is flexible to handle multiple robotic tasks. GPU platform has higher computational capacities and easy-touse development frameworks, so they have been widely adopted in several applications. On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios. With specialized designed hardware logic and algorithm kernels, FPGA-based accelerators can surpass CPU and GPU in performance and energy efficiency. In this paper, we give an overview of previous work on FPGA-based robotic accelerators covering different stages of the robotic system pipeline. An analysis of software and hardware optimization techniques and main technical issues is presented, along with some commercial and space applications, to serve as a guide for future work.