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Paper Abstract and Keywords
Presentation 2019-09-20 11:20
FPGA Implementation of Image Recognition with Multiple Simple Neural Networks for Higher Performance and Accuracy
Musashi Aoto, Shoya Hirukawa, Yasutaka Wada, Kazutaka Maruyama (Meisei Univ) RECONF2019-31
Abstract (in Japanese) (See Japanese page) 
(in English) Object recognition on camera images is one of the most important technologies required to realize automatic driving systems．
Object recognition with deep learning has come to be used in common， and its recognition accuracy can be improved by performing deep learning using a more complex neural network．
However， such deep and complex neural network structures increase the overhead for inference/recognition
As a result， the realtime constraints may be violated instead of its accuracy．
This paper focuses on the environment of autonomous driving competition in FPGA design contests held at international conferences {FPT'18} and {HEART2019}， and does not use a single complex neural network for learning and inference， but simple and recognition．
This paper describes a method to achieve both recognition accuracy and recognition speed by simultaneously using multiple networks with a limited number of targets．
The proposed method can be expected not only to reduce the time required for learning and inference， but also to improve the circuit scale when the FPGA is implemented．
Keyword (in Japanese) (See Japanese page) 
(in English) FPGA / utonomous drivings / CNN / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 208, RECONF2019-31, pp. 57-62, Sept. 2019.
Paper # RECONF2019-31 
Date of Issue 2019-09-12 (RECONF) 
ISSN 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)
Download PDF RECONF2019-31

Conference Information
Committee RECONF  
Conference Date 2019-09-19 - 2019-09-20 
Place (in Japanese) (See Japanese page) 
Place (in English) KITAKYUSHU Convention Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Reconfigurable Systems, etc. 
Paper Information
Registration To RECONF 
Conference Code 2019-09-RECONF 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) FPGA Implementation of Image Recognition with Multiple Simple Neural Networks for Higher Performance and Accuracy 
Sub Title (in English)  
Keyword(1) FPGA  
Keyword(2) utonomous drivings  
Keyword(3) CNN  
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1st Author's Name Musashi Aoto  
1st Author's Affiliation Meisei University (Meisei Univ)
2nd Author's Name Shoya Hirukawa  
2nd Author's Affiliation Meisei University (Meisei Univ)
3rd Author's Name Yasutaka Wada  
3rd Author's Affiliation Meisei University (Meisei Univ)
4th Author's Name Kazutaka Maruyama  
4th Author's Affiliation Meisei University (Meisei Univ)
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Speaker Author-1 
Date Time 2019-09-20 11:20:00 
Presentation Time 20 minutes 
Registration for RECONF 
Paper # RECONF2019-31 
Volume (vol) vol.119 
Number (no) no.208 
Page pp.57-62 
#Pages 6 
Date of Issue 2019-09-12 (RECONF) 

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