Paper Abstract and Keywords |
Presentation |
2018-03-08 14:20
A Study of Kernel Clustering for Reducing Memory Footprint of CNN Yuki Matsui, Shinobu Miwa (UEC), Satoshi Shindo, Tomoaki Tsumura (NITech), Hayato Yamaki, Hiroki Honda (UEC) CPSY2017-140 DC2017-96 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Convolutional Neural Network (CNN) is widely used in the field of image recognition due to the high recognition accuracy. CNN is a sort of deep and large-scale neural networks so that it has numbers of parameters to be used for the computation. There have been many studies of compressing the data of CNN such as reducing the numbers of parameters and bits of parameters. Meanwhile, a well-trained CNN has very regular structure (i.e., 2D kernels) available for data compression, but no study of exploiting this structure for data compression in CNN has been reported so far. We have proposed a technique that clusters 2D kernels trained and replaces them with representative 2D kernels for reducing the number of parameters in CNN. In this paper, we report the experimental results of clustering the overall 2D kernels within VGG-16 with various numbers of clusters. Our experimental results show that the proposed technique can reduce the number of kernels by 85.6% in exchange for a 9% reduction in the recognition accuracy. The proposed technique is orthogonal to the other approaches of compressing the data in CNN, such as pruning and quantization; hence, they can be used together to obtain further gains. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
CNN / data compression / clustering / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 479, CPSY2017-140, pp. 185-190, March 2018. |
Paper # |
CPSY2017-140 |
Date of Issue |
2018-02-28 (CPSY, DC) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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CPSY2017-140 DC2017-96 |
Conference Information |
Committee |
CPSY DC IPSJ-SLDM IPSJ-EMB IPSJ-ARC |
Conference Date |
2018-03-07 - 2018-03-08 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Okinoshima Bunka-Kaikan Bldg. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
ETNET2018 |
Paper Information |
Registration To |
CPSY |
Conference Code |
2018-03-CPSY-DC-SLDM-EMB-ARC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study of Kernel Clustering for Reducing Memory Footprint of CNN |
Sub Title (in English) |
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CNN |
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data compression |
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clustering |
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1st Author's Name |
Yuki Matsui |
1st Author's Affiliation |
The University of Electro-Communications (UEC) |
2nd Author's Name |
Shinobu Miwa |
2nd Author's Affiliation |
The University of Electro-Communications (UEC) |
3rd Author's Name |
Satoshi Shindo |
3rd Author's Affiliation |
Nagoya Institute of Technology (NITech) |
4th Author's Name |
Tomoaki Tsumura |
4th Author's Affiliation |
Nagoya Institute of Technology (NITech) |
5th Author's Name |
Hayato Yamaki |
5th Author's Affiliation |
The University of Electro-Communications (UEC) |
6th Author's Name |
Hiroki Honda |
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The University of Electro-Communications (UEC) |
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Speaker |
Author-1 |
Date Time |
2018-03-08 14:20:00 |
Presentation Time |
25 minutes |
Registration for |
CPSY |
Paper # |
CPSY2017-140, DC2017-96 |
Volume (vol) |
vol.117 |
Number (no) |
no.479(CPSY), no.480(DC) |
Page |
pp.185-190 |
#Pages |
6 |
Date of Issue |
2018-02-28 (CPSY, DC) |
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