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Paper Abstract and Keywords
Presentation 2022-11-24 13:00
Optimal Data Communication Scheduling Considering Multiple Data Compression Techniques in Distributed Deep Learning
Fukuda Ryudai, Takuji Tachibana (Univ. Fukui) NS2022-105
Abstract (in Japanese) (See Japanese page) 
(in English) In distributed deep learning, which uses multiple processors, the training time can be greatly reduced by executing the training process on each processor. However, it is necessary to communicate the training results between processors, and the size of the communication data greatly affects the total training time. Although it is expected that the data size is decreased using a data compression technique, the appropriate compression ratio and processing time vary depending on the training process and transmission method of each layer.
The total training time varies depending on the data compression technique. In this paper, we propose an optimal data communication scheduling that uses multiple data compression techniques in distributed deep learning. The proposed method minimizes the total training time by appropriately using multiple compression techniques with different data compression ratios and processing times in each layer. The performance of the proposed method is evaluated by simulation to investigate the effectiveness of the proposed method. Numerical examples show that the proposed method can reduce the total training time by using appropriate data compression techniques in each layer.
Keyword (in Japanese) (See Japanese page) 
(in English) Distributed deep learning / Back propagation / Data compression / Communication scheduling / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 274, NS2022-105, pp. 29-34, Nov. 2022.
Paper # NS2022-105 
Date of Issue 2022-11-17 (NS) 
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 NS2022-105

Conference Information
Committee NS ICM CQ NV  
Conference Date 2022-11-24 - 2022-11-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Humanities and Social Sciences Center, Fukuoka Univ. + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network quality, Network measurement/management, Network virtualization, Network service, Blockchain, Security, Network intelligence/AI, etc. 
Paper Information
Registration To NS 
Conference Code 2022-11-NS-ICM-CQ-NV 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Optimal Data Communication Scheduling Considering Multiple Data Compression Techniques in Distributed Deep Learning 
Sub Title (in English)  
Keyword(1) Distributed deep learning  
Keyword(2) Back propagation  
Keyword(3) Data compression  
Keyword(4) Communication scheduling  
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1st Author's Name Fukuda Ryudai  
1st Author's Affiliation University of Fukui (Univ. Fukui)
2nd Author's Name Takuji Tachibana  
2nd Author's Affiliation University of Fukui (Univ. Fukui)
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Speaker Author-1 
Date Time 2022-11-24 13:00:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2022-105 
Volume (vol) vol.122 
Number (no) no.274 
Page pp.29-34 
#Pages 6 
Date of Issue 2022-11-17 (NS) 

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