Memristor-Based Synapse Design and Training Scheme for Neuromorphic Computing Architecture

Abstract

Memristors have been rediscovered recently and then gained increasing attentions. Their unique properties, such as high density, nonvolatility, and recording historic behavior of current (or voltage) profile, have inspired the creation of memristor-based neuromorphic computing architecture. Rather than the existing crossbar-based neuron network designs, we focus on memristor-based synapse and the corresponding training circuit to mimic the real biological system. In this paper, first, the basic synapse design is presented. On top of it, we will discuss the training sharing scheme and explore design implication on multi-synapse neuron system. Energy saving method such as self-training is also investigated.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2012
Accession Number
ADA587910

Entities

People

  • Hai H. Li
  • Hui Wang
  • Robinson Pino

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Circuits
  • Computer Architecture
  • Computer Programming
  • Computing System Architectures
  • Electric Charge
  • Energy Consumption
  • Engineering
  • Films
  • Governments
  • Materials
  • Memristors
  • New York
  • Simulations
  • Thin Films
  • Training
  • Transistors

Readers

  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.