Principal component analysis hardware acceleration

Since machine learning is getting more attention in various applications, the performance of those applications has become the main concern of its users. To perform machine learning, one of the vital processes is feature extraction which is to reduce the raw data dimension that aims to get rid of no...

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Main Author:	Ng, Yee Wei
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93003/1/NgYeeWeiMSKE2020.pdf
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id	my-utm-ep.93003
record_format	uketd_dc
spelling	my-utm-ep.930032021-11-07T06:00:20Z Principal component analysis hardware acceleration 2020 Ng, Yee Wei TK Electrical engineering. Electronics Nuclear engineering Since machine learning is getting more attention in various applications, the performance of those applications has become the main concern of its users. To perform machine learning, one of the vital processes is feature extraction which is to reduce the raw data dimension that aims to get rid of noise and speed up further data analysis. Principal Component Analysis (PCA) is one of dimension reduction techniques that often used with other complex feature extraction techniques. However, PCA involves heavy computation and plays an important role to determine the speed performance of the application. This project is to propose PCA hardware acceleration to enhance its performance. From software profiling, the most intensive function in the PCA algorithm is the computation of eigenvalues and eigenvectors (eigensolver). Thus, this project has developed an eigensolver hardware accelerator by applying parallel execution through unrolling, pipelining and scheduling techniques in order to improve the performance of PCA. The proposed eigensolver is developed using Vivado HLS 2019.2. The performance of the proposed accelerator is evaluated by comparing it with conventional PCA hardware. The proposed eigensolver hardware accelerator has achieved a speedup of 6.27 compared with its conventional implementation. 2020 Thesis http://eprints.utm.my/id/eprint/93003/ http://eprints.utm.my/id/eprint/93003/1/NgYeeWeiMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135896 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 Ng, Yee Wei Principal component analysis hardware acceleration
description	Since machine learning is getting more attention in various applications, the performance of those applications has become the main concern of its users. To perform machine learning, one of the vital processes is feature extraction which is to reduce the raw data dimension that aims to get rid of noise and speed up further data analysis. Principal Component Analysis (PCA) is one of dimension reduction techniques that often used with other complex feature extraction techniques. However, PCA involves heavy computation and plays an important role to determine the speed performance of the application. This project is to propose PCA hardware acceleration to enhance its performance. From software profiling, the most intensive function in the PCA algorithm is the computation of eigenvalues and eigenvectors (eigensolver). Thus, this project has developed an eigensolver hardware accelerator by applying parallel execution through unrolling, pipelining and scheduling techniques in order to improve the performance of PCA. The proposed eigensolver is developed using Vivado HLS 2019.2. The performance of the proposed accelerator is evaluated by comparing it with conventional PCA hardware. The proposed eigensolver hardware accelerator has achieved a speedup of 6.27 compared with its conventional implementation.
format	Thesis
qualification_level	Master's degree
author	Ng, Yee Wei
author_facet	Ng, Yee Wei
author_sort	Ng, Yee Wei
title	Principal component analysis hardware acceleration
title_short	Principal component analysis hardware acceleration
title_full	Principal component analysis hardware acceleration
title_fullStr	Principal component analysis hardware acceleration
title_full_unstemmed	Principal component analysis hardware acceleration
title_sort	principal component analysis hardware acceleration
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/93003/1/NgYeeWeiMSKE2020.pdf
_version_	1747818624288030720

Principal component analysis hardware acceleration

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