Support vector machine hardware accelerator for tongue colour diagnosis

Tongue body features such as colour is used in Traditional Chinese Medicine (TMC) practices to diagnose a patient’s state of health. However, the diagnosis of one patient’s health condition varies between practitioners. This gives rise to the need of a standard method such as using Support Vector Ma...

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Main Author:	Thiah, Amanda Su Lin
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93135/1/ThiahSuLinMSKE2020.pdf
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id	my-utm-ep.93135
record_format	uketd_dc
spelling	my-utm-ep.931352021-11-19T03:23:54Z Support vector machine hardware accelerator for tongue colour diagnosis 2020 Thiah, Amanda Su Lin TK Electrical engineering. Electronics Nuclear engineering Tongue body features such as colour is used in Traditional Chinese Medicine (TMC) practices to diagnose a patient’s state of health. However, the diagnosis of one patient’s health condition varies between practitioners. This gives rise to the need of a standard method such as using Support Vector Machine to identify the tongue body colour. SVM is a supervised machine learning algorithm that aims to explore the characteristics and correlations between the gathered data to deduce the most efficient way in classifying the data into groups. Typically, SVM classifiers are implemented in software where the classification performance is very dependent on the architecture of the general-purpose CPU. Since classification of tongue images is a recurring event, the design of a hardware accelerator is explored in this project. The purpose of design a hardware accelerator is to boost the classifier performance, execution time and latency so that it meets real-time constraints. Architectural optimization methods, such as loop unrolling, memory array partitioning, pipelining and adder tree implementation of the SVM classification algorithm are performed in Xilinx’s Vivado HLS and later synthesized to target for FPGA implementation. To further optimize the resource utilization, 18-bits IEEE-754 floating-point representations for the floating point units are used. The SVM hardware is able to demonstrate 140x speed up with similar classification accuracy when compared to the software implementation in MATLAB. 2020 Thesis http://eprints.utm.my/id/eprint/93135/ http://eprints.utm.my/id/eprint/93135/1/ThiahSuLinMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135982 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 Thiah, Amanda Su Lin Support vector machine hardware accelerator for tongue colour diagnosis
description	Tongue body features such as colour is used in Traditional Chinese Medicine (TMC) practices to diagnose a patient’s state of health. However, the diagnosis of one patient’s health condition varies between practitioners. This gives rise to the need of a standard method such as using Support Vector Machine to identify the tongue body colour. SVM is a supervised machine learning algorithm that aims to explore the characteristics and correlations between the gathered data to deduce the most efficient way in classifying the data into groups. Typically, SVM classifiers are implemented in software where the classification performance is very dependent on the architecture of the general-purpose CPU. Since classification of tongue images is a recurring event, the design of a hardware accelerator is explored in this project. The purpose of design a hardware accelerator is to boost the classifier performance, execution time and latency so that it meets real-time constraints. Architectural optimization methods, such as loop unrolling, memory array partitioning, pipelining and adder tree implementation of the SVM classification algorithm are performed in Xilinx’s Vivado HLS and later synthesized to target for FPGA implementation. To further optimize the resource utilization, 18-bits IEEE-754 floating-point representations for the floating point units are used. The SVM hardware is able to demonstrate 140x speed up with similar classification accuracy when compared to the software implementation in MATLAB.
format	Thesis
qualification_level	Master's degree
author	Thiah, Amanda Su Lin
author_facet	Thiah, Amanda Su Lin
author_sort	Thiah, Amanda Su Lin
title	Support vector machine hardware accelerator for tongue colour diagnosis
title_short	Support vector machine hardware accelerator for tongue colour diagnosis
title_full	Support vector machine hardware accelerator for tongue colour diagnosis
title_fullStr	Support vector machine hardware accelerator for tongue colour diagnosis
title_full_unstemmed	Support vector machine hardware accelerator for tongue colour diagnosis
title_sort	support vector machine hardware accelerator for tongue colour diagnosis
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/93135/1/ThiahSuLinMSKE2020.pdf
_version_	1747818636558467072

Support vector machine hardware accelerator for tongue colour diagnosis

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