Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing

O. Krestinskaya, Alex James Pappachen

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

1 Citation (Scopus)

Abstract

The memristive crossbar aims to implement analog weighted neural network, however, the realistic implementation of such crossbar arrays is not possible due to limited switching states of memristive devices. In this work, we propose the design of an analog deep neural network with binary weight update through backpropagation algorithm using binary state memristive devices. We show that such networks can be successfully used for image processing task and has the advantage of lower power consumption and small on-chip area in comparison with digital counterparts. The proposed network was benchmarked for MNIST handwritten digits recognition achieving an accuracy of approximately 90%.

Original languageEnglish
Title of host publication18th International Conference on Nanotechnology, NANO 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781538653364
DOIs
Publication statusPublished - Jan 24 2019
Event18th International Conference on Nanotechnology, NANO 2018 - Cork, Ireland
Duration: Jul 23 2018Jul 26 2018

Publication series

NameProceedings of the IEEE Conference on Nanotechnology
Volume2018-July
ISSN (Print)1944-9399
ISSN (Electronic)1944-9380

Conference

Conference18th International Conference on Nanotechnology, NANO 2018
CountryIreland
CityCork
Period7/23/187/26/18

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ASJC Scopus subject areas

  • Bioengineering
  • Electrical and Electronic Engineering
  • Materials Chemistry
  • Condensed Matter Physics

Cite this

Krestinskaya, O., & James Pappachen, A. (2019). Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing. In 18th International Conference on Nanotechnology, NANO 2018 [8626224] (Proceedings of the IEEE Conference on Nanotechnology; Vol. 2018-July). IEEE Computer Society. https://doi.org/10.1109/NANO.2018.8626224