Pattern Classification with Memristive Crossbar Circuits

Abstract

Neuromorphic pattern classifiers were implemented, for the first time, using transistor-free integrated crossbar circuits with bilayer metal-oxide memristors. 106- and 108-crosspoint neuromorphic networks were trained in-situ using a Manhattan-Rule algorithm to separate a set of 33 binary images: into 3 classes using the batch-mode training, and into 4 classes using the stochastic-mode training, respectively. Simulation of much larger, multilayer neural network classifiers based on such technology has shown that their fidelity may be on a par with the state-of-the-art results.

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 2016
Accession Number
AD1025246

Entities

People

  • Dmitri B Strukov

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Circuits
  • Classification
  • Complementary Metal-Oxide Semiconductors
  • Convolutional Neural Networks
  • Deep Learning
  • Emerging Technology
  • Energy Efficiency
  • Machine Learning
  • Memristors
  • Metal Oxides
  • Networks
  • Neural Networks
  • Reliability
  • Simulations
  • Switching
  • Titanium Oxides

Readers

  • Computational Modeling and Simulation
  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks