Digital systems for neural networks

Paolo Ienne; Gary Kuhn

doi:10.1117/12.204207

25 April 1995 Digital systems for neural networks

Paolo Ienne, Gary Kuhn

Proceedings Volume 10279, Digital Signal Processing Technology: A Critical Review; 102790G (1995) https://doi.org/10.1117/12.204207
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Paolo Ienne and Gary Kuhn "Digital systems for neural networks", Proc. SPIE 10279, Digital Signal Processing Technology: A Critical Review, 102790G (25 April 1995); https://doi.org/10.1117/12.204207

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