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| Presentation | 2023-03-01 11:50 Acceleration of Memristor Modeling Based on Machine Learning Using Gaussian Process Yuta Shintani, Michiko Inoue (Naist), Michihiro Shintani (Kyoto Institute of Technology)  VLD2022-75 HWS2022-46 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | There has been a great deal of research into the development of domain-specific circuits for multiply-and-accumulate processing, which play a central role in machine learning by interpreting new physical phenomena into computing and storage capabilities. On the other hand, these devices have an unresolved operation principle, and therefore, compact models that are essential for circuit design cannot be created with sufficient accuracy. In this study, we propose a compact modeling method based on Gaussian processes for memristors. Although modeling methods based on machine learning have been proposed in the past, only the accuracy of reproduction by SPICE has been evaluated, and long learning times have not been sufficiently discussed. The proposed method reduces the learning time by using a Gaussian process and also reduces the inference time in SPICE simulation by considering sparsity. Evaluation using data of memristor devices obtained by actual measurements shows that the proposed method is more than 3,239 times faster than existing methods using LSTM (long short-term memory), and that inference on a commercial SPICE simulator can be performed with the same accuracy and computation time. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Compact modeling / Gaussian process regression / Memoristor / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 402, VLD2022-75, pp. 13-18, March 2023. | |
| Paper # | VLD2022-75 | |
| Date of Issue | 2023-02-22 (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 | VLD2022-75 HWS2022-46 |
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| Conference Information | |
| Committee | HWS VLD |
| Conference Date | 2023-03-01 - 2023-03-04 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | VLD |
| Conference Code | 2023-03-HWS-VLD |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Acceleration of Memristor Modeling Based on Machine Learning Using Gaussian Process |
| Sub Title (in English) | |
| Keyword(1) | Compact modeling |
| Keyword(2) | Gaussian process regression |
| Keyword(3) | Memoristor |
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| 1st Author's Name | Yuta Shintani |
| 1st Author's Affiliation | Nara Institute of Science and Technology (Naist) |
| 2nd Author's Name | Michiko Inoue |
| 2nd Author's Affiliation | Nara Institute of Science and Technology (Naist) |
| 3rd Author's Name | Michihiro Shintani |
| 3rd Author's Affiliation | Kyoto Institute of Technology (Kyoto Institute of Technology) |
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| Speaker | Author-1 |
| Date Time | 2023-03-01 11:50:00 |
| Presentation Time | 25 minutes |
| Registration for | VLD |
| Paper # | VLD2022-75, HWS2022-46 |
| Volume (vol) | vol.122 |
| Number (no) | no.402(VLD), no.403(HWS) |
| Page | pp.13-18 |
| #Pages | 6 |
| Date of Issue | 2023-02-22 (VLD, HWS) |
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