Image recognition using capsule network on FPGA

A capsule neural network (CapsNet) is a new approach in artificial neural network (ANN) that produces a better model hierarchical relationship. A capsule is a set of neurons. Each capsule generates vector which presents the details of an entity. The performance of CapsNet on graphics processing unit...

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Main Author:	Adrees, Salim Ali Abdulrraziq
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93142/1/SalimAliAbdulrraziqMSKE2020.pdf
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id	my-utm-ep.93142
record_format	uketd_dc
spelling	my-utm-ep.931422021-11-19T03:23:56Z Image recognition using capsule network on FPGA 2020 Adrees, Salim Ali Abdulrraziq TK Electrical engineering. Electronics Nuclear engineering A capsule neural network (CapsNet) is a new approach in artificial neural network (ANN) that produces a better model hierarchical relationship. A capsule is a set of neurons. Each capsule generates vector which presents the details of an entity. The performance of CapsNet on graphics processing unit (GPU) is considerably better than convolutional neural network (CNN) at recognizing highly overlapping digits in images. Nevertheless, this new method has not been designed as accelerator on field-programmable gate array (FPGA) to measure the speedup performance and compared it with the GPU. This is because of the lack of hardware design experience. This project aims to design the CapsNet model (accelerator) on FPGA using high-level synthesis (HLS). Then, the performance between FPGA and GPU will be compared, mainly in terms speedup and accuracy. Behavioural module is synthesized using HLS tools on FPGA then it is evaluated and validated using MNIST dataset. The module is designed to receive features vectors of handwritten digits image as an input and pass it through several layers to predict the output. The speed-up performance on FPGA is expected to be higher than GPU, but FPGA accuracy is expected to be slightly lower than GPU. The module can be useful in detecting the license plate of fast-moving vehicles and many other applications. 2020 Thesis http://eprints.utm.my/id/eprint/93142/ http://eprints.utm.my/id/eprint/93142/1/SalimAliAbdulrraziqMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135983 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Adrees, Salim Ali Abdulrraziq Image recognition using capsule network on FPGA
description	A capsule neural network (CapsNet) is a new approach in artificial neural network (ANN) that produces a better model hierarchical relationship. A capsule is a set of neurons. Each capsule generates vector which presents the details of an entity. The performance of CapsNet on graphics processing unit (GPU) is considerably better than convolutional neural network (CNN) at recognizing highly overlapping digits in images. Nevertheless, this new method has not been designed as accelerator on field-programmable gate array (FPGA) to measure the speedup performance and compared it with the GPU. This is because of the lack of hardware design experience. This project aims to design the CapsNet model (accelerator) on FPGA using high-level synthesis (HLS). Then, the performance between FPGA and GPU will be compared, mainly in terms speedup and accuracy. Behavioural module is synthesized using HLS tools on FPGA then it is evaluated and validated using MNIST dataset. The module is designed to receive features vectors of handwritten digits image as an input and pass it through several layers to predict the output. The speed-up performance on FPGA is expected to be higher than GPU, but FPGA accuracy is expected to be slightly lower than GPU. The module can be useful in detecting the license plate of fast-moving vehicles and many other applications.
format	Thesis
qualification_level	Master's degree
author	Adrees, Salim Ali Abdulrraziq
author_facet	Adrees, Salim Ali Abdulrraziq
author_sort	Adrees, Salim Ali Abdulrraziq
title	Image recognition using capsule network on FPGA
title_short	Image recognition using capsule network on FPGA
title_full	Image recognition using capsule network on FPGA
title_fullStr	Image recognition using capsule network on FPGA
title_full_unstemmed	Image recognition using capsule network on FPGA
title_sort	image recognition using capsule network on fpga
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/93142/1/SalimAliAbdulrraziqMSKE2020.pdf
_version_	1747818637769572352

Image recognition using capsule network on FPGA

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