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Paper Abstract and Keywords
Presentation 2021-03-03 10:00
Energy Efficient Approximate Storing to MRAM for Deep Neural Network Tasks in Edge Computing
Yoshinori Ono, Kimiyoshi Usami (SIT) VLD2020-67 HWS2020-42
Abstract (in Japanese) (See Japanese page) 
(in English) On-chip learning is gaining attention in edge devices. In addition, a magnetic RAM (MRAM) is a promising memory technology for edge devices because of low leakage energy. However, the high write energy is a disadvantage of MRAM. For minimizing the write energy, we propose an approximate storing approach to MRAM for learning tasks of deep neural networks (DNN). The proposed approach writes the weight and bias data to MRAM approximately on each epoch with the fine-grained adjusted write time. Simulation results with image recognition DNN applications have demonstrated that the write energy can be reduced in the range from 9% to 37% with negligible (< 0.5%) accuracy loss.
Keyword (in Japanese) (See Japanese page) 
(in English) MRAM / Energy Minimization / Approximate Computing / Deep Learning / On-chip Learning / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 400, VLD2020-67, pp. 1-6, March 2021.
Paper # VLD2020-67 
Date of Issue 2021-02-24 (VLD, HWS) 
ISSN 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 VLD2020-67 HWS2020-42

Conference Information
Committee HWS VLD  
Conference Date 2021-03-03 - 2021-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Design Technology for System-on-Silicon, Hardware Security, etc. 
Paper Information
Registration To VLD 
Conference Code 2021-03-HWS-VLD 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Energy Efficient Approximate Storing to MRAM for Deep Neural Network Tasks in Edge Computing 
Sub Title (in English)  
Keyword(1) MRAM  
Keyword(2) Energy Minimization  
Keyword(3) Approximate Computing  
Keyword(4) Deep Learning  
Keyword(5) On-chip Learning  
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1st Author's Name Yoshinori Ono  
1st Author's Affiliation Shibaura Institute of Technology (SIT)
2nd Author's Name Kimiyoshi Usami  
2nd Author's Affiliation Shibaura Institute of Technology (SIT)
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Speaker Author-1 
Date Time 2021-03-03 10:00:00 
Presentation Time 25 minutes 
Registration for VLD 
Paper # VLD2020-67, HWS2020-42 
Volume (vol) vol.120 
Number (no) no.400(VLD), no.401(HWS) 
Page pp.1-6 
#Pages
Date of Issue 2021-02-24 (VLD, HWS) 


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