Demonstration of Generative Adversarial Network by Intrinsic Random Noises of Analog RRAM Devices

Yudeng Lin, Huaqiang Wu, Bin Gao, Peng Yao, Wei Wu, Qingtian Zhang, Xiang Zhang, Xinyi Li, Fuhai Li, Jiwu Lu, Gezi Li, Shimeng Yu, He Qian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

For the first time, Generative Adversarial Network (GAN) is experimentally demonstrated on 1kb analog RRAM array. After online training, the network can generate different patterns of digital numbers. The intrinsic random noises of analog RRAM device are utilized as the input of the neural network to improve the diversity of the generated numbers. The impacts of read and write noises on the performance of GAN are analyzed. Optimized methodology is developed to mitigate the excessive noise effect on RRAM based GAN. This work proves that RRAM is suitable for the application of GAN. It also paves a new way to take advantage of the non-ideal effects of RRAM devices.

Original languageEnglish (US)
Title of host publication2018 IEEE International Electron Devices Meeting, IEDM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3.4.1-3.4.4
ISBN (Electronic)9781728119878
DOIs
StatePublished - Jan 16 2019
Event64th Annual IEEE International Electron Devices Meeting, IEDM 2018 - San Francisco, United States
Duration: Dec 1 2018Dec 5 2018

Publication series

NameTechnical Digest - International Electron Devices Meeting, IEDM
Volume2018-December
ISSN (Print)0163-1918

Conference

Conference64th Annual IEEE International Electron Devices Meeting, IEDM 2018
Country/TerritoryUnited States
CitySan Francisco
Period12/1/1812/5/18

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Electrical and Electronic Engineering
  • Materials Chemistry

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