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Paper Abstract and Keywords
Presentation 2018-08-01 15:15
Optimization of Numerical Expression in CNN using Genetic Algorithm
Wakana Nogami (Tokyo Univ./AIST), Tsutomu Ikegami, Shin-ichi O'uchi, Ryosei Takano (AIST), Yuma Kishi, Tomohiro Kudoh (Tokyo Univ./AIST) CPSY2018-26
Abstract (in Japanese) (See Japanese page) 
(in English) The accuracy of image recognition by the convolutional neural network (CNN) has been improving year by year, and the models are becoming more complicated and larger. One way to reduce the model size is to use a low bit-width numerical expression. There are many types of research to reduce the bit-width by using floating-point, fixed-point, ternary, and binary arithmetics, and so on. They are aiming at (1) simplifying the computation (2) by introducing a less-bit arithmetic (3) to keep the image recognition accuracy. Considering ease of computation, it is appropriate to use floating-point and fixed-point. Therefore, we introduced a variable bin size quantization and found the appropriate numerical expression from the viewpoint of low bit-width and high accuracy by optimizing the bin size using a genetic algorithm. In this study, we targeted on quantization of trained parameters at inference. As a result, we succeeded in finding a numerical expression that can give higher Top-1 Accuracy than when using fixed-point or floating-point type for our CNN models. The numerical expression is relatively similar to fixed-point type. We also found that by using this numerical expression, it is possible to reduce the bit-width down to 3-bit without decreasing the accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) CNN / Adaptive Quantization / Genetic Algorithm / Numerical Expression / Optimization / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 165, CPSY2018-26, pp. 193-198, July 2018.
Paper # CPSY2018-26 
Date of Issue 2018-07-23 (CPSY) 
ISSN Online edition: ISSN 2432-6380
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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 2018-07-30 - 2018-08-01 
Place (in Japanese) (See Japanese page) 
Place (in English) Kumamoto City International Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Parallel, Distributed and Cooperative Processing Systems and Dependable Computing 
Paper Information
Registration To CPSY 
Conference Code 2018-07-CPSY-DC-ARC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Optimization of Numerical Expression in CNN using Genetic Algorithm 
Sub Title (in English)  
Keyword(1) CNN  
Keyword(2) Adaptive Quantization  
Keyword(3) Genetic Algorithm  
Keyword(4) Numerical Expression  
Keyword(5) Optimization  
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Keyword(8)  
1st Author's Name Wakana Nogami  
1st Author's Affiliation The University of Tokyo/National Institute of Advanced Industrial And Technology (Tokyo Univ./AIST)
2nd Author's Name Tsutomu Ikegami  
2nd Author's Affiliation National Institute of Advanced Industrial And Technology (AIST)
3rd Author's Name Shin-ichi O'uchi  
3rd Author's Affiliation National Institute of Advanced Industrial And Technology (AIST)
4th Author's Name Ryosei Takano  
4th Author's Affiliation National Institute of Advanced Industrial And Technology (AIST)
5th Author's Name Yuma Kishi  
5th Author's Affiliation The University of Tokyo/National Institute of Advanced Industrial And Technology (Tokyo Univ./AIST)
6th Author's Name Tomohiro Kudoh  
6th Author's Affiliation The University of Tokyo/National Institute of Advanced Industrial And Technology (Tokyo Univ./AIST)
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Speaker Author-1 
Date Time 2018-08-01 15:15:00 
Presentation Time 30 minutes 
Registration for CPSY 
Paper # CPSY2018-26 
Volume (vol) vol.118 
Number (no) no.165 
Page pp.193-198 
#Pages
Date of Issue 2018-07-23 (CPSY) 


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