Paper Abstract and Keywords |
Presentation |
2021-08-18 09:30
[Invited Talk]
Analog in-memory computing in FeFET based 1T1R array for low-power edge AI applications Daisuke Saito, Toshiyuki Kobayashi, Hiroki Koga (SONY), Yusuke Shuto, Jun Okuno, Kenta Konishi (SSS), Masanori Tsukamoto, Kazunobu Ohkuri (SONY), Taku Umebayashi (SSS), Takayuki Ezaki (SONY) SDM2021-36 ICD2021-7 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Deep neural network (DNN) inference for edge AI requires low-power operation, which can be achieved by implementing massively parallel matrix-vector multiplications (MVM) in the analog domain on a highly resistive memory array. We propose a 1T1R compute cell (1T1R-cell) using a ferroelectric hafnium oxide-based FET (FeFET) and TiN/SiO2 tunneling junction of MΩ resistor (MOR) for analog in-memory computing (AiMC). The MOR exhibited a tunneling current behavior and MΩ resistance. A 1T1R-cell array-level evaluation was also performed. A random access for writing with low write disturbance scheme was confirmed from the summation-DC-current output, and binaries were successfully classified into “T” and “L.” Based on the experimental results of our proposed 1T1R-cell, we obtained a state-of-the-art energy efficiency of 13700 TOPS/W including the periphery. Furthermore, we confirmed that a high inference accuracy can be obtained with our low-resistance-variability 1T1R-cell with a properly trained model. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
deep neural network / 1T1R-cell / analog in-memory computing / matrix-vector multiplication / FeFET / tunneling junction resistor / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 138, SDM2021-36, pp. 33-37, Aug. 2021. |
Paper # |
SDM2021-36 |
Date of Issue |
2021-08-10 (SDM, ICD) |
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 |
SDM2021-36 ICD2021-7 |
|