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Article

Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application

1
Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, Beijing Institute of Technology, Beijing 100081, China
2
Information Processing Department, Shanghai Aerospace Electronic Technology Institute, Shanghai 201108, China
*
Author to whom correspondence should be addressed.
Academic Editor: Claudio Persello
Remote Sens. 2022, 14(13), 3130; https://doi.org/10.3390/rs14133130
Received: 9 May 2022 / Revised: 24 June 2022 / Accepted: 24 June 2022 / Published: 29 June 2022
(This article belongs to the Section Remote Sensing Image Processing)
In recent years, convolutional neural network (CNN)-based algorithms have been widely used in remote sensing image processing and show tremendous performance in a variety of application fields. However, large amounts of data and intensive computations make the deployment of CNN-based algorithms a challenging problem, especially for the spaceborne scenario where resources and power consumption are limited. To tackle this problem, this paper proposes an automatic CNN deployment solution on resource-limited field-programmable gate arrays (FPGAs) for spaceborne remote sensing applications. Firstly, a series of hardware-oriented optimization methods are proposed to reduce the complexity of the CNNs. Secondly, a hardware accelerator is designed. In this accelerator, a reconfigurable processing engine array with efficient convolutional computation architecture is used to accelerate CNN-based algorithms. Thirdly, to bridge the optimized CNNs and hardware accelerator, a compilation toolchain is introduced into the deployment solution. Through the automatic conversion from CNN models to hardware instructions, various networks can be deployed on hardware in real-time. Finally, we deployed an improved VGG16 network and an improved YOLOv2 network on Xilinx AC701 to evaluate the effectiveness of the proposed deployment solution. The experiments show that with only 3.407 W power consumption and 94 DSP consumption, our solution achieves 23.06 giga operations per second (GOPS) throughput in the improved VGG16 and 22.17 GOPS throughput in the improved YOLOv2. Compared to the related works, the DSP efficiency of our solution is improved by 1.3–2.7×. View Full-Text
Keywords: remote sensing; convolutional neural networks (CNNs); optimization; field-programmable gate array (FPGA); compilation toolchain remote sensing; convolutional neural networks (CNNs); optimization; field-programmable gate array (FPGA); compilation toolchain
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MDPI and ACS Style

Yan, T.; Zhang, N.; Li, J.; Liu, W.; Chen, H. Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application. Remote Sens. 2022, 14, 3130. https://doi.org/10.3390/rs14133130

AMA Style

Yan T, Zhang N, Li J, Liu W, Chen H. Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application. Remote Sensing. 2022; 14(13):3130. https://doi.org/10.3390/rs14133130

Chicago/Turabian Style

Yan, Tianwei, Ning Zhang, Jie Li, Wenchao Liu, and He Chen. 2022. "Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application" Remote Sensing 14, no. 13: 3130. https://doi.org/10.3390/rs14133130

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