Imperial College London
Professor Wayne Luk

ProfessorWayneLuk

Faculty of Engineering, Department of Computing

Professor of Computer Engineering
 
 
 
//

Contact

 

+44 (0)20 7594 8313w.luk Website

 
 
//

Location

 

434Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@inproceedings{Liu:2019:10.1007/978-3-030-17227-5_2,
author = {Liu, S and Chu, RSW and Wang, X and Luk, W},
doi = {10.1007/978-3-030-17227-5_2},
pages = {17--31},
title = {Optimizing CNN-Based Hyperspectral Image Classification on FPGAs},
url = {http://dx.doi.org/10.1007/978-3-030-17227-5_2},
year = {2019}
}
Download

RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Hyperspectral image (HSI) classification has been widely adopted in remote sensing imagery analysis applications which require high classification accuracy and real-time processing speed. Convolutional neural networks (CNNs)-based methods have been proven to achieve state-of-the-art accuracy in classifying HSIs. However, CNN models are often too computationally intensive to achieve real-time response due to the high dimensional nature of HSI, compared to traditional methods such as Support Vector Machines (SVMs). Besides, previous CNN models used in HSI are not specially designed for efficient implementation on embedded devices such as FPGAs. This paper proposes a novel CNN-based algorithm for HSI classification which takes into account hardware efficiency and thus is more hardware friendly compared to prior CNN models. An optimized and customized architecture which maps the proposed algorithm on FPGA is then proposed to support real-time on-board classification with low power consumption. Implementation results show that our proposed accelerator on a Xilinx Zynq 706 FPGA board achieves more than 70 $$\times $$ faster than an Intel 8-core Xeon CPU and 3 $$\times $$ faster than an NVIDIA GeForce 1080 GPU. Compared to previous SVM-based FPGA accelerators, we achieve comparable processing speed but provide a much higher classification accuracy.
AU  - Liu,S
AU  - Chu,RSW
AU  - Wang,X
AU  - Luk,W
DO  - 10.1007/978-3-030-17227-5_2
EP  - 31
PY  - 2019///
SN  - 0302-9743
SP  - 17
TI  - Optimizing CNN-Based Hyperspectral Image Classification on FPGAs
UR  - http://dx.doi.org/10.1007/978-3-030-17227-5_2
ER  -
Download