Analog Very Large Scale Integration (VLSI) Implementations of Artificial Neural Networks

Abstract

There has been a recent resurgence of interest in the multi- disciplinary field of artificial neural networks. Artificial neural networks, originally inspired by the computational capabilities of the human brain, refer to a variety of computing architectures that consist of massively parallel interconnections of simple processing elements. Currently, there exist two promising advanced technologies for implementing neural networks: Very Large Scale Integrated (VLSI) circuits and optical. This final technical report describes the utilization of VLSI circuits for implementing various neural networks, with an emphasis on analog VLSI. A comparison of the different implementation techniques is provided, as is the type of paradigm implemented (e.g., backpropagation, hopfield, bidirectional associative memories, etc.). Artificial Neural Networks, Analog VLSI.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1992
Accession Number
ADA256621

Entities

People

  • Michael L. Hinman

Organizations

  • Rome Laboratory

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Charge Carriers
  • Charge Coupled Devices
  • Circuits
  • Complementary Metal-Oxide Semiconductors
  • Computations
  • Computers
  • Electronics Laboratories
  • Field Effect Transistors
  • Integrated Circuits
  • Metal Oxide Semiconductors
  • Modulation
  • Networks
  • Neural Networks
  • Physical Properties
  • Semiconductors
  • Three Dimensional
  • Very Large Scale Integration

Fields of Study

  • Computer science

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks