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| 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 |
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| 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 | |
| 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 | VLD2021-84 HWS2021-61 |
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| 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 |
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| 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 |
| #Pages | 1 |
| Date of Issue | 2022-02-28 (VLD, HWS) |
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