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Content Provider | IET Digital Library |
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Author | Hou, Yong Chen, Zhang Bao |
Abstract | Deep learning is a branch of traditional machine learning. Thanks to the leap in computer hardware computing power in recent years, deep learning has become one of the hot topics. In view of the inefficiency of the traditional LeNet-5, here, LeNet-5 is improved by using the FPGA to accelerate the LeNet-5. Finally, by training the handwritten digit recognition model experiment, it is verified that the improved LeNet-5 model has a great improvement in terms of efficiency and accuracy. |
Starting Page | 526 |
Ending Page | 528 |
Page Count | 3 |
Volume Number | 2020 |
e-ISSN | 20513305 |
Issue Number | Issue 13, Jul (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2020/13 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1190 |
Journal | The Journal of Engineering |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2020-01-13 |
Access Restriction | Open |
Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
Subject Keyword | Communication Computing Computer Communication Computer Hardware Computing Power Computer Network Computer Network And Technique Computer Vision And Image Processing Technique Deep Learning Field Programmable Gate Array FPGA Acceleration Handwritten Character Recognition Handwritten Digit Recognition Model Experiment Image Recognition Improved LeNet-5 Model Knowledge Engineering Technique Learning in AI LeNet-5 Improvement Logic And Switching Circuit Logic Circuit Telecommunication Computing Traditional LeNet-5 |
Content Type | Text |
Resource Type | Article |
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