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| Presentation | 2019-09-20 14:00 Quantized Neural Networks Library for Exact Hardware Emulation Masato Kiyama, Yasuhiro Nakahara, Motoki Amagasaki, Masahiro Iida (Kumamoto Univ.)  RECONF2019-33 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Deep neural networks (DNNs) have recently shown outstanding performance in many application domains. However, it is difficult to run much complex and larger DNN applications on mobile devices due to limited hardware resources. Quantization is used to reduce the hardware requirements because it uses fewer bits, such as 8-bit fixed points instead of 32-bit floating-point numbers. DNN librarys use floating-point numbers after quantization because they assume that all data to be calculated in floating-point format. Therefore, there is mismatch in running hardware emulation exactly. In this paper, we propose a method of calculation for exact hardware emulation and developed a new DNNs library based on our method. We show that our library is capable of exact hardware emulation. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | quantization / deep learning / / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 208, RECONF2019-33, pp. 69-74, Sept. 2019. | |
| Paper # | RECONF2019-33 | |
| Date of Issue | 2019-09-12 (RECONF) | |
| 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 | RECONF2019-33 |
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| Conference Information | |
| Committee | RECONF |
| Conference Date | 2019-09-19 - 2019-09-20 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | KITAKYUSHU Convention Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Reconfigurable Systems, etc. |
| Paper Information | |
| Registration To | RECONF |
| Conference Code | 2019-09-RECONF |
| Language | English (Japanese title is available) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Quantized Neural Networks Library for Exact Hardware Emulation |
| Sub Title (in English) | |
| Keyword(1) | quantization |
| Keyword(2) | deep learning |
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| 1st Author's Name | Masato Kiyama |
| 1st Author's Affiliation | Kumamoto University (Kumamoto Univ.) |
| 2nd Author's Name | Yasuhiro Nakahara |
| 2nd Author's Affiliation | Kumamoto University (Kumamoto Univ.) |
| 3rd Author's Name | Motoki Amagasaki |
| 3rd Author's Affiliation | Kumamoto University (Kumamoto Univ.) |
| 4th Author's Name | Masahiro Iida |
| 4th Author's Affiliation | Kumamoto University (Kumamoto Univ.) |
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| Speaker | Author-1 |
| Date Time | 2019-09-20 14:00:00 |
| Presentation Time | 20 minutes |
| Registration for | RECONF |
| Paper # | RECONF2019-33 |
| Volume (vol) | vol.119 |
| Number (no) | no.208 |
| Page | pp.69-74 |
| #Pages | 6 |
| Date of Issue | 2019-09-12 (RECONF) |
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