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Proceedings Paper

Digital systems for neural networks
Author(s): Paolo Ienne; Gary Kuhn
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Paper Abstract

Neural networks are non-linear static or dynamical systems that learn to solve problems from examples. Those learning algorithms that require a lot of computing power could benefit from fast dedicated hardware. This paper presents an overview of digital systems to implement neural networks. We consider three options for implementing neural networks in digital systems: serial computers, parallel systems with standard digital components, and parallel systems with special-purpose digital devices. We describe many examples under each option, with an emphasis on commercially available systems. We discuss the trend toward more general architectures, we mention a few hybrid and analog systems that can complement digital systems, and we try to answer questions that came to our minds as prospective users of these systems. We conclude that support software and in general, system integration, is beginning to reach the level of versatility that many researchers will require. The next step appears to be integrating all of these technologies together, in a new generation of big, fast and user-friendly neurocomputers.

Paper Details

Date Published: 25 April 1995
PDF: 32 pages
Proc. SPIE 10279, Digital Signal Processing Technology: A Critical Review, 102790G (25 April 1995); doi: 10.1117/12.204207
Show Author Affiliations
Paolo Ienne, EPFL Microcomputing Lab. (Switzerland)
Gary Kuhn, Siemens Corporate Research (United States)


Published in SPIE Proceedings Vol. 10279:
Digital Signal Processing Technology: A Critical Review
Panos Papamichalis; Robert D. Kerwin, Editor(s)

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