Paper Abstract and Keywords |
Presentation |
2020-01-24 13:50
Ternarized Backpropagation for Edge AI and its FPGA Implementation Tatsuya Kaneko, Yoshiharu Yamagishi, Hiroshi Momose, Tetsuya Asai (Hokkaido Univ.) NLP2019-95 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In recent years there has been growing more interest in machine/deep learning.
As following this movement, many types of hardware architecture is proposed to accelerate AI processing.
These hardware AI is studied both the cloud domain and the edge domain.
While great strides have been made in cloud-base AI, edge-base AI remains unsolved problem from the perspective of technical issues.
A method maintaining the quality while reducing the power and the resource is required.
We implemented low-resources and low-power training algorithm, ternarized backpropagation (TBP), on FPGA and achieved reducing 16.7¥% of logic elements, 13.6¥% of registers and 49.8¥% of SRAM usage. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Edge-AI / Training Algorithm / FPGA / Quantization / Low-power / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 381, NLP2019-95, pp. 53-58, Jan. 2020. |
Paper # |
NLP2019-95 |
Date of Issue |
2020-01-16 (NLP) |
ISSN |
Print edition: ISSN 0913-5685 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) |
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NLP2019-95 |
Conference Information |
Committee |
NLP NC |
Conference Date |
2020-01-23 - 2020-01-25 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Miyakojima Marine Terminal |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
NLP |
Conference Code |
2020-01-NLP-NC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Ternarized Backpropagation for Edge AI and its FPGA Implementation |
Sub Title (in English) |
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Keyword(1) |
Edge-AI |
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Training Algorithm |
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FPGA |
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Quantization |
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Low-power |
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1st Author's Name |
Tatsuya Kaneko |
1st Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
2nd Author's Name |
Yoshiharu Yamagishi |
2nd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
3rd Author's Name |
Hiroshi Momose |
3rd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
4th Author's Name |
Tetsuya Asai |
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Hokkaido University (Hokkaido Univ.) |
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Speaker |
Author-1 |
Date Time |
2020-01-24 13:50:00 |
Presentation Time |
20 minutes |
Registration for |
NLP |
Paper # |
NLP2019-95 |
Volume (vol) |
vol.119 |
Number (no) |
no.381 |
Page |
pp.53-58 |
#Pages |
6 |
Date of Issue |
2020-01-16 (NLP) |
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