Analog memristive time dependent learning using discrete nanoscale RRAM devices

Aniket Singha, Bhaskaran Muralidharan, Bipin Rajendran

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

7 Scopus citations

Abstract

We propose a scheme that mimics the analog time dependent learning characteristics of biological synapses using a small set of discrete nanoscale RRAM devices whose switching voltages vary stochastically. Using numerical models and simulations, we demonstrate that a voltage limited analog memristor operating in the tunneling regime and a parallel combination of 10 RRAM devices having discrete resistance states (two resistance states high and low), can both be employed as artificial synapses with similar statistical performance. We also show that by appropriately choosing the programming voltages and hence the switching probability of the RRAM devices, it is possible to tune the relative conductance of the synaptic element anywhere in the range of 2-100. This paper thus shows the possibility of using discrete RRAM devices to realize an analog functionality in artificial learning systems.

Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2248-2255
Number of pages8
ISBN (Electronic)9781479914845
DOIs
StatePublished - Sep 3 2014
Externally publishedYes
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: Jul 6 2014Jul 11 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/TerritoryChina
CityBeijing
Period7/6/147/11/14

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Keywords

  • Memristor
  • Neuromorphic Computing
  • Spike Timing Dependent Plasticity

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