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Exploring the impact of random telegraph noise-induced accuracy loss in Resistive RAM-based deep neural network

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journal contribution
posted on 11.06.2020 by Yide Du, Linglin Jing, Hui Fang, Haibao Chen, Yimao Cai, Runsheng Wang, Jianfu Zhang, Zhigang Ji
For Resistive RAM (RRAM)-based deep neural network, Random telegraph noise (RTN) causes accuracy loss during inference. In this work, we systematically investigated the impact of RTN on the complex deep neural networks (DNNs) with different datasets. By using 8 mainstream DNNs and 4 datasets, we explored the origin that caused the RTN-induced accuracy loss. Based on the understanding, for the first time, we proposed a new method to estimate the accuracy loss. The method was verified with other 10 DNN/dataset combinations that were not used for establishing the method. Finally, we discussed its potential adoption for the co-optimization of the DNN architecture and the RRAM technology, paving ways to RTN-induced accuracy loss mitigation for future neuromorphic hardware systems.

Funding

National Key R&D Program of China under the grant no. 2019YFB2205000

National Natural Science Foundation of China under the grant no. 61927901

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Electron Devices

Volume

67

Issue

8

Pages

3335 - 3340

Publisher

Institute of Electrical and Electronics Engineers

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

08/06/2020

Publication date

2020-06-26

Copyright date

2020

ISSN

0018-9383

Language

en

Depositor

Dr Hui Fang Deposit date: 8 June 2020

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