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| Presentation | 2022-01-20 15:10 [Short Paper] A Study of Joint Control of Machine Learning Model and Wireless LAN Parameters in Split inference by Reinforcement Learning Kojin Yorita (Tokyo Tech.), Sohei Itahara (Kyoto Univ.), Takayuki Nishio (Tokyo Tech.), Daiki Yoda, Toshihisa Nabetani (Toshiba)  SeMI2021-66 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Distributed inference (DI) enables machine learning (ML) inference with a deep neural network on resource-constrained devices. However, lossy wireless networks can become a bottleneck, thereby increasing latency in DI. This paper studies a joint control of the wireless communication parameters (e.g., transmission rate, retransmission limit) and ML model architecture to reduce latency while maintaining inference accuracy. The proposed method focuses on the packet-loss tolerance of ML inference and uses unreliable (i.e., high packet-loss rate) but low-latency communication protocol. To achieve a well-balanced trade-off between accuracy and latency, the proposed method jointly controls wireless communication parameters affecting the reliability and latency and ML model architecture affecting accuracy and packet-loss reliance, based on multi-armed bandit (MAB) algorithm, namely upper confidence bound (UCB) algorithm. The results of ns3-ai-based computer simulations show that the proposed method reduces communication latency while maintaining inference accuracy. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Distributed inference / Joint control / Machine learning / Wireless LAN / Reinforcement Learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 333, SeMI2021-66, pp. 51-54, Jan. 2022. | |
| Paper # | SeMI2021-66 | |
| Date of Issue | 2022-01-13 (SeMI) | |
| 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 | SeMI2021-66 |
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| Conference Information | |
| Committee | SeMI |
| Conference Date | 2022-01-20 - 2022-01-21 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SeMI |
| Conference Code | 2022-01-SeMI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study of Joint Control of Machine Learning Model and Wireless LAN Parameters in Split inference by Reinforcement Learning |
| Sub Title (in English) | |
| Keyword(1) | Distributed inference |
| Keyword(2) | Joint control |
| Keyword(3) | Machine learning |
| Keyword(4) | Wireless LAN |
| Keyword(5) | Reinforcement Learning |
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| 1st Author's Name | Kojin Yorita |
| 1st Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech.) |
| 2nd Author's Name | Sohei Itahara |
| 2nd Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 3rd Author's Name | Takayuki Nishio |
| 3rd Author's Affiliation | Tokyo Institute of Technology (Tokyo Tech.) |
| 4th Author's Name | Daiki Yoda |
| 4th Author's Affiliation | Toshiba (Toshiba) |
| 5th Author's Name | Toshihisa Nabetani |
| 5th Author's Affiliation | Toshiba (Toshiba) |
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| Speaker | Author-1 |
| Date Time | 2022-01-20 15:10:00 |
| Presentation Time | 10 minutes |
| Registration for | SeMI |
| Paper # | SeMI2021-66 |
| Volume (vol) | vol.121 |
| Number (no) | no.333 |
| Page | pp.51-54 |
| #Pages | 4 |
| Date of Issue | 2022-01-13 (SeMI) |
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