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Proceedings Paper

Artificial neural networks: principles and VLSI implementation
Author(s): Jan Van der Spiegel; Paul Mueller; David Blackman; Christopher Donham; Ralph Etienne-Cummings; Pervez Aziz; A. Choudhury; L. Jones; J. Xin
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Paper Abstract

This paper gives an overview of the principles and hardware realizations of artificial neural networks. The first section describes the operation of neural networks, using simple examples to illustrate some of its key properties. Next the different architectures are described, including single and multiple perceptron networks, Hopfield and Kohonen nets. A brief discussion of the learning rules employed in feedforward and feedback networks follows. The final section discusses hardware implementations of neural systems with emphasis on analog VLSI. Different approaches for the realizations of neurons and synapses are described. A brief comparison between analog and digital techniques is given.

Paper Details

Date Published: 1 November 1990
PDF: 14 pages
Proc. SPIE 1405, 5th Congress of the Brazilian Society of Microelectronics, (1 November 1990); doi: 10.1117/12.26307
Show Author Affiliations
Jan Van der Spiegel, Univ. of Pennsylvania (United States)
Paul Mueller, Univ. of Pennsylvania (United States)
David Blackman, Univ. of Pennsylvania (United States)
Christopher Donham, Univ. of Pennsylvania (United States)
Ralph Etienne-Cummings, Univ. of Pennsylvania (United States)
Pervez Aziz, Univ. of Pennsylvania (United States)
A. Choudhury, Univ. of Pennsylvania (United States)
L. Jones, Univ. of Pennsylvania (United States)
J. Xin, Univ. of Pennsylvania (United States)

Published in SPIE Proceedings Vol. 1405:
5th Congress of the Brazilian Society of Microelectronics
Vitor Baranauskas, Editor(s)

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