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Paper Abstract and Keywords
Presentation 2022-03-07 13:40
[Memorial Lecture] DistriHD: A Memory Efficient Distributed Binary Hyperdimensional Computing Architecture for Image Classification
Dehua Liang, Jun Shiomi, Noriyuki Miura (Osaka Univ.), Hiromitsu Awano (Kyoto Univ.) VLD2021-84 HWS2021-61
Abstract (in Japanese) (See Japanese page) 
(in English) Hyper-Dimensional (HD) computing is a brain-inspired learning approach for efficient and fast learning on today’s embedded devices. HD computing first encodes all data points to high-dimensional vectors called hypervectors and then efficiently performs the classification task using a well-defined set of operations. Although HD computing achieved reasonable performances in several practical tasks, it comes with huge memory requirements since the data point should be stored in a very long vector having thousands of bits. To alleviate this problem, we propose a novel HD computing architecture, called DistriHD which enables HD computing to be trained and tested using binary hypervectors and achieves high accuracy in single-pass training mode with significantly low hardware resources. DistriHD encodes data points to distributed binary hypervectors and eliminates the expensive item memory in the encoder, which significantly reduces the required hardware cost for inference. Our evaluation also shows that our model can achieve a 27.6× reduction in memory cost without hurting the classification accuracy. The hardware implementation also demonstrates that DistriHD achieves over 9.9× and 28.8× reduction in area and power, respectively.
Keyword (in Japanese) (See Japanese page) 
(in English) Brain-inspired Computing / Hyper-Dimensional Computing / Memory-Efficiency / Distributed System / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 412, VLD2021-84, pp. 44-44, March 2022.
Paper # VLD2021-84 
Date of Issue 2022-02-28 (VLD, HWS) 
ISSN Online edition: ISSN 2432-6380
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 VLD2021-84 HWS2021-61

Conference Information
Committee VLD HWS  
Conference Date 2022-03-07 - 2022-03-08 
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 2022-03-VLD-HWS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) DistriHD: A Memory Efficient Distributed Binary Hyperdimensional Computing Architecture for Image Classification 
Sub Title (in English)  
Keyword(1) Brain-inspired Computing  
Keyword(2) Hyper-Dimensional Computing  
Keyword(3) Memory-Efficiency  
Keyword(4) Distributed System  
1st Author's Name Dehua Liang  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Jun Shiomi  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Noriyuki Miura  
3rd Author's Affiliation Osaka University (Osaka Univ.)
4th Author's Name Hiromitsu Awano  
4th Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2022-03-07 13:40:00 
Presentation Time 25 minutes 
Registration for VLD 
Paper # VLD2021-84, HWS2021-61 
Volume (vol) vol.121 
Number (no) no.412(VLD), no.413(HWS) 
Page p.44 
Date of Issue 2022-02-28 (VLD, HWS) 

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