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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
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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)
Download PDF 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)  
Keyword(1) Deep Learning  
Keyword(2) CNN  
Keyword(3) FPGA  
Keyword(4) 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
Date of Issue 2020-01-15 (VLD, CPSY, RECONF) 


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