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The algorithms that run on cell phones tend to be specially optimized and quality-reduced neural networks. For example, https://arxiv.org/abs/1704.04861
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard, Menglong Zhu, Bo Chen
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Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam
2017 · arXiv
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From the abstract
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.
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Neutral mention · rule
I doubt pruning alone is going to serve up the same kind of wins that energy aware architectures will. The main problem is that convolutions are heavy computational operations. There has been ongoing research in modifications to the convolution that basically decrease the amount of computation being done in channel space (grouped and depthwise separable convolutions). The tradeoff is accuracy, but new techniques (like shuffling the channels) are helping recover that. MobileNets and ShuffleNet are some examples. They see 10x reductions in MACs, far more than the "73 percent reduction in power consumption over the standard implementation of neural networks" talked about in this article. https://arxiv.org/pdf/1704.04861.pdf https://arxiv.org/pdf/1707.01083.pdf
Neutral mention · rule
I believe they are referring to this paper titled "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" https://arxiv.org/pdf/1704.04861.pdf
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