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VLSI implementations of CNNs for image processing and vision tasks: single and multiple chip approaches

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5 Author(s)
Anguita, M. ; Dept. de Electron. y Tecnologia de Computadores, Granada Univ., Spain ; Pelayo, F.J. ; Ros, E. ; Palomar, D.
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Three alternative VLSI analog implementations of cellular neural networks (CNNs) are described and demonstrated with fabricated and tested chips, which have been devised to perform image processing and vision tasks: a programmable low-power CNN with embedded photosensors; a compact fixed-template CNN based on unipolar current-mode signals; and basic CMOS circuits to build an extended and biologically-inspired CNN model using spikes. The first two VLSI approaches are intended for focal-plane image processing applications. The third one allows, since its dynamics is defined by process-independent local ratios and its input/output can be efficiently multiplexed in time, the construction of very large multiple chip CNNs for more complex vision tasks

Published in:

Cellular Neural Networks and their Applications, 1996. CNNA-96. Proceedings., 1996 Fourth IEEE International Workshop on

Date of Conference:

24-26 Jun 1996