Fast Image Clustering on Network of Mobile Phones

  • Jorge Ortiz - jjortiz@us.ibm.com
  • Chien-Chin Huang - huang@cs.nyu.edu
  • Supriyo Chakraborty - supriyo@us.ibm.com

Machine learning algorithms, in conjunction with user data, hold the promise of revolutionizing the way we interact with our phones, and indeed their widespread adoption in the design of apps bear testimony to this promise. However, currently, the computationally expensive segments of the learning pipeline, such as feature extraction and model training, are offloaded to the cloud, resulting in an over-reliance on the network and under-utilization of computing resources available on mobile platforms. In this paper, we show that by combining the computing power distributed over a number of phones, judicious optimization choices, and contextual information it is possible to execute the end-to-end pipeline entirely on the phones at the edge of the network, efficiently. Our results show a 75% improvement over the standard, full-pipeline implementation running on the phones without modification.

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Suyog Gupta,
Jun 24, 2016, 12:52 PM
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