Memristor-Based Synaptic Sampling Machines

I. Dolzhikova, K. Salama, V. Kizheppatt, Alex James Pappachen

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


Synaptic Sampling Machine (SSM) is a type of neural network model that considers biological unreliability of the synapses. We propose the circuit design of the SSM neural network which is realized through the memristive-CMOS crossbar structure with the synaptic sampling cell (SSC) being used as a basic stochastic unit. The increase in the edge computing devices in the Internet of things era, drives the need for hardware acceleration for data processing and computing. The computational considerations of the processing speed and possibility for the real-time realization pushes the synaptic sampling algorithm that demonstrated promising results on software for hardware implementation.

Original languageEnglish
Title of host publication18th International Conference on Nanotechnology, NANO 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781538653364
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
ISSN (Print)1944-9399
ISSN (Electronic)1944-9380


Conference18th International Conference on Nanotechnology, NANO 2018

ASJC Scopus subject areas

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

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