Corpus ID: 6621072

Are Very Deep Neural Networks Feasible on Mobile Devices

@inproceedings{Rallapalli2016AreVD,
  title={Are Very Deep Neural Networks Feasible on Mobile Devices},
  author={S. Rallapalli and Hang Qiu and A. J. Bency and S. Karthikeyan and R. Govindan and B. S. Manjunath and R. Urgaonkar},
  year={2016}
}

S. Rallapalli, Hang Qiu, +4 authors R. Urgaonkar

Published 2016

Computer Science

In the recent years, the computing power of mobile devices has increased tremendously, a trend that is expected to continue in the future. [...] Key Method We then quantify the performance of several deep CNNspecific memory management techniques, some of which leverage the observation that these CNNs have a small number of layers that require most of the memory. We find that a particularly novel approach that offloads these bottleneck layers to the mobile device’s CPU and pipelines frame processing is a…Expand Abstract

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