Implementing Artificial Neural Networks in Integrated Circuitry: A design Proposal for Back-Propagation

Abstract

In an attempt to develop CMOS circuitry (both analog and digital) for the implementation of artificial neural networks, the back-propagation learning algorithm was examined in detail. Simulations were performed to determine to robustness of this algorithm to anticipated implementation artifacts such as quantization and weight-range limitation. Circuitry which computes with analog signals and digitally encoded weights was then designed to implement the algorithm within the tolerances determined by the simulations. The architecture of an alternative, fully digital design was also defined and its performance compared with that of both the analog/digital design and a fully analog design based on circuitry that has been proposed in the literature.

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Document Details

Document Type
Technical Report
Publication Date
Nov 18, 1988
Accession Number
ADA202541

Entities

People

  • S. L. Gilbert

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Amplifiers
  • Analog Signals
  • Artifacts
  • Artificial Intelligence Software
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Electrical Engineering
  • Feedback Amplifiers
  • Machine Learning
  • Neural Networks
  • Signal Processing
  • Simulations
  • Two Dimensional

Readers

  • Computer Programming and Software Development.
  • Neural Network Machine Learning.
  • Software Engineering

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks