Design for a Manufacturing Method for Memristor-Based Neuromorphic Computing Processors

Abstract

It is well received that conventional CMOS technology is approaching its physical limitations. Researchers have started to explore the potential replacement by leveraging the advances of nanotechnology. Very recently, memristor attracted growing attentions since the first physical realization reported by HP Labs in 2008. Unique characteristics like non-volatility, re-configurability, and analog state storage make memristor become a very promising candidate for the realization of artificial neural systems. In this project, we developed a SPICE-compatible model of memristor and designed CMOS-mimicked memristor cells for system development. Then we proposed a memristor-based design of bidirectional transmission excitation/inhibition synapses and implemented a neuromorphic computing system based on our proposed synapse designs. The robustness of our system is also evaluated by considering the actual manufacturing variability with the emphasis on process variations. After that, we discussed memristor-based crossbar neuromorphic architecture. Finally, we compared the designs of synapse network-based and crossbar-based neuromorphic computing systems.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2013
Accession Number
ADA581795

Entities

People

  • Beiye Liu
  • Yiran Chen

Organizations

  • University of Pittsburgh

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Capacitance
  • Circuits
  • Computer Programming
  • Computers
  • Electric Fields
  • Electron Density
  • Electrons
  • Excitation
  • Inhibition
  • Logic Gates
  • Memristors
  • Neural Networks
  • Simulations
  • Standards
  • Synapses

Readers

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

Technology Areas

  • Biotechnology