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

Du, Y, Jing, L, Fang, H, Chen, H, Cai, Y, Wang, R, Zhang, JF and Ji, Z (2020) Exploring the impact of random telegraph noise-induced accuracy loss in Resistive RAM-based deep neural network. IEEE Transactions on Electron Devices, 67 (8). pp. 3335-3340. ISSN 0018-9383

TED-RTN-NN-SHJT-2020-Accepted.pdf - Accepted Version

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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 without going through time-consuming RTN simulation. 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.

Item Type: Article
Additional Information: © 2020 IEEE. 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.
Uncontrolled Keywords: 0906 Electrical and Electronic Engineering
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Electronics & Electrical Engineering (merged with Engineering 10 Aug 20)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date Deposited: 11 Jun 2020 08:55
Last Modified: 19 Aug 2022 10:30
DOI or ID number: 10.1109/TED.2020.3002736
URI: https://researchonline.ljmu.ac.uk/id/eprint/13084
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