Convolution and max pooling layer accelerator for convolutional neural network

Convolutional Neural Network (CNN) are widely used in the field of computer vision and show its great advantages in image classification, object recognition, video surveillance. Hence, the performance of CNN playing more important role during the development of the application which applying CNN alg...

Full description

Saved in:

Bibliographic Details
Main Author:	Goh, Jinn Chyn
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93001/1/GohJinnChynMSKE2020.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.93001
record_format	uketd_dc
spelling	my-utm-ep.930012021-11-07T06:00:20Z Convolution and max pooling layer accelerator for convolutional neural network 2020 Goh, Jinn Chyn TK Electrical engineering. Electronics Nuclear engineering Convolutional Neural Network (CNN) are widely used in the field of computer vision and show its great advantages in image classification, object recognition, video surveillance. Hence, the performance of CNN playing more important role during the development of the application which applying CNN algorithm. In this paper, an accelerator is developed for improving the performance of CNN. The proposed accelerator targeted the most computation intensive functions in CNN, which are convolution and max pooling. The developed accelerator is targeting on CNN with 64 x 64 input image size, 5 x 5 filter size and 2 x 2 max pooling.. By using Vivado, the period, clock cycle and resources required to run convolution and max pooling are measured. Three proposed methodology are combined to enhance the performance of CNN: (i) unrolling (ii) pipelining (iii) combination of convolution and max pooling layer. Tradeoff between the performance and hardware cost required to build the accelerator are simulated and analyzed. The performance of the new proposed accelerator are proven to be four times better and with limited increase of the hardware cost, addition of 60% of logic gates compared to the existing work. 2020 Thesis http://eprints.utm.my/id/eprint/93001/ http://eprints.utm.my/id/eprint/93001/1/GohJinnChynMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135863 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 Goh, Jinn Chyn Convolution and max pooling layer accelerator for convolutional neural network
description	Convolutional Neural Network (CNN) are widely used in the field of computer vision and show its great advantages in image classification, object recognition, video surveillance. Hence, the performance of CNN playing more important role during the development of the application which applying CNN algorithm. In this paper, an accelerator is developed for improving the performance of CNN. The proposed accelerator targeted the most computation intensive functions in CNN, which are convolution and max pooling. The developed accelerator is targeting on CNN with 64 x 64 input image size, 5 x 5 filter size and 2 x 2 max pooling.. By using Vivado, the period, clock cycle and resources required to run convolution and max pooling are measured. Three proposed methodology are combined to enhance the performance of CNN: (i) unrolling (ii) pipelining (iii) combination of convolution and max pooling layer. Tradeoff between the performance and hardware cost required to build the accelerator are simulated and analyzed. The performance of the new proposed accelerator are proven to be four times better and with limited increase of the hardware cost, addition of 60% of logic gates compared to the existing work.
format	Thesis
qualification_level	Master's degree
author	Goh, Jinn Chyn
author_facet	Goh, Jinn Chyn
author_sort	Goh, Jinn Chyn
title	Convolution and max pooling layer accelerator for convolutional neural network
title_short	Convolution and max pooling layer accelerator for convolutional neural network
title_full	Convolution and max pooling layer accelerator for convolutional neural network
title_fullStr	Convolution and max pooling layer accelerator for convolutional neural network
title_full_unstemmed	Convolution and max pooling layer accelerator for convolutional neural network
title_sort	convolution and max pooling layer accelerator for convolutional neural network
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/93001/1/GohJinnChynMSKE2020.pdf
_version_	1747818623806734336

Convolution and max pooling layer accelerator for convolutional neural network

Similar Items