Spin torque nano-oscillator based Oscillatory Neural Network

Chamika M. Liyanagedera, Karthik Yogendra, Kaushik Roy, Deliang Fan

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

2 Scopus citations

Abstract

Oscillatory Neural Networks (ONN) are becoming a popular neuromorphic computing model owing to their efficient parallel processing capabilities. Hoppensteadt and Izhikevich proposed an ONN architecture resembling associative memory, with Phase-Locked Loop (PLL) circuits as neurons. Unfortunately, there are shortcomings in realizing such architectures due to the inefficiencies of CMOS based implementations of oscillators and other hardware. We propose a PLL structure for ONN applications fashioned using energy efficient and scalable Spin Torque Oscillators (STOs). We demonstrate the functionality of a 60 neuron ONN using STOs for binary image identification.

Original languageEnglish (US)
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1387-1394
Number of pages8
ISBN (Electronic)9781509006199
DOIs
StatePublished - Oct 31 2016
Externally publishedYes
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: Jul 24 2016Jul 29 2016

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2016-October

Conference

Conference2016 International Joint Conference on Neural Networks, IJCNN 2016
CountryCanada
CityVancouver
Period7/24/167/29/16

Keywords

  • Associative memory
  • Frequency locking
  • LLG
  • Oscillatory neural network
  • Phase locked loop
  • Phase locking
  • Spin torque oscillators

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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