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Keyword: battery-powered devices ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning

   Yang, Chen and Sze · arXiv (Cornell University) · 2016

   The authors propose a pruning method for convolutional neural networks that uses estimated energy consumption, not just model size, to decide which weights to remove, and report energy savings on AlexNet and GoogLeNet.

   Unchecked2 claims
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   1. UncheckedEnergy-aware pruning cut AlexNet's estimated energy use 3.7-fold and GoogLeNet's 1.6-fold, with under 1% loss in top-5 accuracy.“With the proposed pruning method, the energy consumption of AlexNet and GoogLeNet are reduced by 3.7x and 1.6x, respectively, with less than 1% top-5 accuracy loss.”
   2. UncheckedPruning AlexNet for fewer target classes can greatly cut the number of weights, but the paper reports only limited energy savings from doing so.“Finally, we show that pruning the AlexNet with a reduced number of target classes can greatly decrease the number of weights but the energy reduction is limited.”