POWER SCALABLE IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS


Modi, Sankalp, Wilson, Peter and Brown, Andrew (2005) POWER SCALABLE IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS. In, IEEE International Conference on Electronics, Circuits and Systems (ICECS), IEEE.

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Description/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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Event Dates: December 2005
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > EEE
ePrint ID: 261635
Date Deposited: 09 Dec 2005
Last Modified: 27 Mar 2014 20:04
Publisher: IEEE
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/261635

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