Online training on RRAM based neuromorphic network: Experimental demonstration and operation scheme optimization

Peng Yao, Huaqiang Wu, Bin Gao, Ning Deng, Shimeng Yu, He Qian

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

3 Scopus citations

Abstract

In this work, online training is experimentally demonstrated on a neuromorphic network built with 1k-bit 1T1R RRAM array. The 1T1R RRAM cells in the array exhibit excellent synaptic behavior. Patterns with up to 11.83% noises can be correctly classified by the network after training. Based on the analysis of experimental results, we find the device characteristics and operation schemes significantly affect the system performance. The impact of voltage dependence and asymmetry effects are evaluated, and optimization guidelines are provided.

Original languageEnglish (US)
Title of host publication2017 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages182-183
Number of pages2
ISBN (Electronic)9781509046591
DOIs
StatePublished - Jun 13 2017
Event2017 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2017 - Toyama, Japan
Duration: Feb 28 2017Mar 2 2017

Other

Other2017 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2017
CountryJapan
CityToyama
Period2/28/173/2/17

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Keywords

  • network
  • RRAM
  • synaptic behavior

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Yao, P., Wu, H., Gao, B., Deng, N., Yu, S., & Qian, H. (2017). Online training on RRAM based neuromorphic network: Experimental demonstration and operation scheme optimization. In 2017 IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2017 - Proceedings (pp. 182-183). [7947592] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EDTM.2017.7947592