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FPGA implementation of handwritten number recognition using artificial neural network

Implementation of Deep Learning and Machine Learning Algorithms is always a challenge as they consume a lot of resources and power. In this paper, we have presented a very simple yet efficient way for creating an IP (intellectual property) core for Handwritten Number Recognition for FPGAs. The propo...

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Main Authors: Mittal, Harsh, Sharma, Abhishek, Perumal, Thinagaran
Format: Conference or Workshop Item
Language:English
Published: IEEE 2019
Online Access:http://psasir.upm.edu.my/id/eprint/78077/1/FPGA%20implementation%20of%20handwritten%20number%20recognition%20using%20artificial%20neural%20network.pdf
http://psasir.upm.edu.my/id/eprint/78077/
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spelling my.upm.eprints.780772020-06-02T03:10:38Z http://psasir.upm.edu.my/id/eprint/78077/ FPGA implementation of handwritten number recognition using artificial neural network Mittal, Harsh Sharma, Abhishek Perumal, Thinagaran Implementation of Deep Learning and Machine Learning Algorithms is always a challenge as they consume a lot of resources and power. In this paper, we have presented a very simple yet efficient way for creating an IP (intellectual property) core for Handwritten Number Recognition for FPGAs. The proposed ANN was verified and compared with several ANN networks on MATLAB, which gave the accuracy of about 99.38%. This network was implemented on Xilinx Zybo board XC7Z010CLG400-1. The total area covered by the IP block is 27.9%. The IP created is efficient and uses fewer resources thus suitable for other embedded applications. IEEE 2019 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/78077/1/FPGA%20implementation%20of%20handwritten%20number%20recognition%20using%20artificial%20neural%20network.pdf Mittal, Harsh and Sharma, Abhishek and Perumal, Thinagaran (2019) FPGA implementation of handwritten number recognition using artificial neural network. In: 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), 15-18 Oct. 2019, Osaka, Japan. (pp. 1010-1011). 10.1109/GCCE46687.2019.9015236
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Implementation of Deep Learning and Machine Learning Algorithms is always a challenge as they consume a lot of resources and power. In this paper, we have presented a very simple yet efficient way for creating an IP (intellectual property) core for Handwritten Number Recognition for FPGAs. The proposed ANN was verified and compared with several ANN networks on MATLAB, which gave the accuracy of about 99.38%. This network was implemented on Xilinx Zybo board XC7Z010CLG400-1. The total area covered by the IP block is 27.9%. The IP created is efficient and uses fewer resources thus suitable for other embedded applications.
format Conference or Workshop Item
author Mittal, Harsh
Sharma, Abhishek
Perumal, Thinagaran
spellingShingle Mittal, Harsh
Sharma, Abhishek
Perumal, Thinagaran
FPGA implementation of handwritten number recognition using artificial neural network
author_facet Mittal, Harsh
Sharma, Abhishek
Perumal, Thinagaran
author_sort Mittal, Harsh
title FPGA implementation of handwritten number recognition using artificial neural network
title_short FPGA implementation of handwritten number recognition using artificial neural network
title_full FPGA implementation of handwritten number recognition using artificial neural network
title_fullStr FPGA implementation of handwritten number recognition using artificial neural network
title_full_unstemmed FPGA implementation of handwritten number recognition using artificial neural network
title_sort fpga implementation of handwritten number recognition using artificial neural network
publisher IEEE
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/78077/1/FPGA%20implementation%20of%20handwritten%20number%20recognition%20using%20artificial%20neural%20network.pdf
http://psasir.upm.edu.my/id/eprint/78077/
_version_ 1669008854051979264
score 13.160551

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