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Status: Unchecked ×Keyword: low-power mobile devices ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

   Zhang, Zhou, Lin and Sun · arXiv (Cornell University) · 2017

   The paper presents ShuffleNet, a very low-computation neural network for mobile devices, using two new operations, and reports strong results on ImageNet classification and MS COCO object detection.

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   1. UncheckedShuffleNet uses pointwise group convolution and channel shuffle to cut computation substantially while keeping accuracy.“The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy.”
   2. UncheckedShuffleNet is reported to have a 7.8% lower absolute top-1 error than MobileNet on ImageNet classification at a 40 MFLOPs computation budget.“Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet on ImageNet classification task, under the computation budget of 40 MFLOPs.”
   3. UncheckedOn an ARM-based mobile device, ShuffleNet runs about 13 times faster than AlexNet in practice while keeping comparable accuracy.“On an ARM-based mobile device, ShuffleNet achieves ~13x actual speedup over AlexNet while maintaining comparable accuracy.”