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Paper Abstract and Keywords
Presentation 2017-07-27 15:45
Consideration of All Binarized Convolutional Neural Network
Masayuki Shimoda, Tomoya Fujii, Haruyoshi Yonekawa, Shimpei Sato, Hiroki Nakahara (Tokyo Inst. of Tech.) CPSY2017-28
Abstract (in Japanese) (See Japanese page) 
(in English) A pre-trained convolutional neural network (CNN) is a feed-forward computation perspective, which is widely used for the embedded systems, requires high power-and-area efficiency. This paper realizes a binarized CNN which treats only binary values (+1/-1) for the inputs, the weights and the activation value. In this case, the multiplier is replaced into an XNOR circuit instead of a dedicated DSP block. Both inputs and weights are more suitable for hardware implementation. However, first convolutional layer still calculates in integer precision, since input value is not binarized one. In this paper, we transform input value into maps of which each pixel is 1 bit precision. The proposed method enables a binarized CNN to use bitwise operation in all layers of convolution. We call this all binarized CNN. We conduct experiment on comparing all binarized CNN, floating-point CNN and binarized CNN. Since all binarized CNN do not need dedicated DSP block, area of all binarized CNN is smaller than that of conventional binarized CNN.
Keyword (in Japanese) (See Japanese page) 
(in English) Neural Network / Convolutional Neural Network / Binary Convolutional Neural Network / / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 153, CPSY2017-28, pp. 131-136, July 2017.
Paper # CPSY2017-28 
Date of Issue 2017-07-19 (CPSY) 
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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Conference Information
Committee CPSY DC IPSJ-ARC  
Conference Date 2017-07-26 - 2017-07-28 
Place (in Japanese) (See Japanese page) 
Place (in English) Akita Atorion-Building (Akita) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Parallel, Distributed and Cooperative Processing 
Paper Information
Registration To CPSY 
Conference Code 2017-07-CPSY-DC-ARC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Consideration of All Binarized Convolutional Neural Network 
Sub Title (in English)  
Keyword(1) Neural Network  
Keyword(2) Convolutional Neural Network  
Keyword(3) Binary Convolutional Neural Network  
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1st Author's Name Masayuki Shimoda  
1st Author's Affiliation Tokyo Institude of Technology (Tokyo Inst. of Tech.)
2nd Author's Name Tomoya Fujii  
2nd Author's Affiliation Tokyo Institude of Technology (Tokyo Inst. of Tech.)
3rd Author's Name Haruyoshi Yonekawa  
3rd Author's Affiliation Tokyo Institude of Technology (Tokyo Inst. of Tech.)
4th Author's Name Shimpei Sato  
4th Author's Affiliation Tokyo Institude of Technology (Tokyo Inst. of Tech.)
5th Author's Name Hiroki Nakahara  
5th Author's Affiliation Tokyo Institude of Technology (Tokyo Inst. of Tech.)
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Speaker Author-1 
Date Time 2017-07-27 15:45:00 
Presentation Time 30 minutes 
Registration for CPSY 
Paper # CPSY2017-28 
Volume (vol) vol.117 
Number (no) no.153 
Page pp.131-136 
#Pages 6 
Date of Issue 2017-07-19 (CPSY) 

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