Neural Network Simulation at Warp Speed: How We Got 17 Million Connections per Second

Abstract

We describe a fast back-propagation algorithm for a linear array of processors. Results of an implementation of this algorithm on Warp, a ten processor, programmable systolic array computer, are reviewed and compared with back-propagation implementations on other machines. Our current Warp simulator is about 8 times faster at simulating the NETtalk text-to-speech network than the fastest back-propagation simulator previously reported in the literature. This fast simulator on Warp is being used routinely in a road recognition experiment for robot navigation at Carnegie Mellon. Our results indicate that linear systolic array machines can be efficient neural network simulators. Planned extensions and improvements to our current algorithm is discussed. (EG)

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 1988
Accession Number
ADA218906

Entities

People

  • David S. Touretzky
  • Dean A. Pomerleau
  • George L. Gusciora
  • H. T. Kung

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Classification
  • Computational Science
  • Computer Programming
  • Computer Science
  • Computers
  • Computing System Architectures
  • Linear Arrays
  • Lisp Programming Language
  • Natural Language Processing
  • Network Architecture
  • Network Simulation
  • Neural Networks
  • Signal Processing
  • Simulations
  • Simulators

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Autonomy