Implementing Recurrent Back-Propagation on the Connection Machine.

Abstract

Pineda's Recurrent back-Propagation algorithm for neural networks has been implemented on the Connection Machine, a massively parallel processor. Two fundamentally different graph architectures underlying the nets were tested-one based on arcs, the other on nodes. Confirming the predominance of communication over computation, performance measurements underscore the necessity to make connections the basic unit of representation. Comparisons between these graphs algorithms lead to important conclusions concerning the parallel implementation of neural nets in both software and hardware. Keywords include: Neural networks; Recurrent back-propagation; and Connection machine. (RH)

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Document Details

Document Type
Technical Report
Publication Date
Dec 02, 1988
Accession Number
ADA203796

Entities

People

  • E. M. Deprit

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computations
  • Computer Programming
  • Computers
  • Content Addressable Memory
  • Convergence
  • Differential Equations
  • Equations
  • Lisp Programming Language
  • Military Research
  • Neural Networks
  • Parallel Computing
  • Parallel Processing
  • Parallel Processors
  • Security
  • Simulators
  • Steady State

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks