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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)


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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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


Local EPrints ID: 261635
PURE UUID: ef9289e8-4a33-4be4-bf5b-7a75c68102f8

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

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

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