Paper Abstract and Keywords |
Presentation |
2020-01-22 17:20
Many Universal Convolution Cores for Ensemble Sparse Convolutional Neural Networks Ryosuke Kuramochi, Youki Sada, Masayuki Shimoda, Shimpei Sato, Hiroki Nakahara (Titech) VLD2019-65 CPSY2019-63 RECONF2019-55 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
A convolutional neural network (CNN) is one of the most successful neural networks and widely used for computer vision tasks.
However, it requires a massive number of multiplication and accumulation (MAC) computa- tions with high-power consumption, and higher recognition accuracy is desired for modern tasks.
In the paper, we apply a sparseness technique to generate a weak classifier to build an ensemble CNN.
We control sparse (zero weight) ratio to make an excellent performance and better recognition accuracy. We propose a universal convolution core to realize variations of modern convolutional operations, and extend it to many cores with pipelining architecture to achieve high-throughput operation.
By setting the sparsity ratio and the number of predictors appropriately, high-speed architectures are realized on the many universal convolution cores while the recognition accuracy is improved compared to the conventional single CNN realization.
We implemented the prototype of many universal convolution cores on the Xilinx Kintex UltraScale+ FPGA, and compared with the desktop GPU realization, it is 3.09 times faster, 4.20 times lower power, and 13.33 times better as for the performance per power. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Deep Learning / CNN / FPGA / Ensemble Learning / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 373, RECONF2019-55, pp. 67-72, Jan. 2020. |
Paper # |
RECONF2019-55 |
Date of Issue |
2020-01-15 (VLD, CPSY, RECONF) |
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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VLD2019-65 CPSY2019-63 RECONF2019-55 |
Conference Information |
Committee |
IPSJ-SLDM RECONF VLD CPSY IPSJ-ARC |
Conference Date |
2020-01-22 - 2020-01-24 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Raiosha, Hiyoshi Campus, Keio University |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
FPGA Applications, etc. |
Paper Information |
Registration To |
RECONF |
Conference Code |
2020-01-SLDM-RECONF-VLD-CPSY-ARC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Many Universal Convolution Cores for Ensemble Sparse Convolutional Neural Networks |
Sub Title (in English) |
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Keyword(1) |
Deep Learning |
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CNN |
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FPGA |
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Ensemble Learning |
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1st Author's Name |
Ryosuke Kuramochi |
1st Author's Affiliation |
Tokyo Institute of Technology (Titech) |
2nd Author's Name |
Youki Sada |
2nd Author's Affiliation |
Tokyo Institute of Technology (Titech) |
3rd Author's Name |
Masayuki Shimoda |
3rd Author's Affiliation |
Tokyo Institute of Technology (Titech) |
4th Author's Name |
Shimpei Sato |
4th Author's Affiliation |
Tokyo Institute of Technology (Titech) |
5th Author's Name |
Hiroki Nakahara |
5th Author's Affiliation |
Tokyo Institute of Technology (Titech) |
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Speaker |
Author-1 |
Date Time |
2020-01-22 17:20:00 |
Presentation Time |
25 minutes |
Registration for |
RECONF |
Paper # |
VLD2019-65, CPSY2019-63, RECONF2019-55 |
Volume (vol) |
vol.119 |
Number (no) |
no.371(VLD), no.372(CPSY), no.373(RECONF) |
Page |
pp.67-72 |
#Pages |
6 |
Date of Issue |
2020-01-15 (VLD, CPSY, RECONF) |
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