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Power scalable implementation of artificial neural networks

Modi, Sankalp, Wilson, Peter and Brown, Andrew (2005) Power scalable implementation of artificial neural networks At IEEE International Conference on Electronics, Circuits and Systems (ICECS), Tunisia.

Record type: Conference or Workshop Item (Paper)

Abstract

As the use of Artificial Neural Network (ANN) in mobile embedded devices gets more pervasive, power consumption of ANN hardware is becoming a major limiting factor. Although considerable research efforts are now directed towards low-power implementations of ANN, the issue of dynamic power scalability of the implemented design has been largely overlooked. In this paper, we discuss the motivation and basic principles for implementing power scaling in ANN Hardware. With the help of a simple example, we demonstrate how power scaling can be achieved with dynamic pruning techniques.

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More information

Published date: 2005
Additional Information: Event Dates: December 2005
Venue - Dates: IEEE International Conference on Electronics, Circuits and Systems (ICECS), Tunisia, 2005-12-01
Organisations: EEE

Identifiers

Local EPrints ID: 261635
URI: http://eprints.soton.ac.uk/id/eprint/261635
PURE UUID: ef9289e8-4a33-4be4-bf5b-7a75c68102f8

Catalogue record

Date deposited: 09 Dec 2005
Last modified: 18 Jul 2017 09:00

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Contributors

Author: Sankalp Modi
Author: Peter Wilson
Author: Andrew Brown

University divisions

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