PicSys: Energy-Efficient Fast Image Search on Distributed Mobile Networks

Noor Felemban, Fidan Mehmeti, Hana Khamfroush, Zongqing Lu, Swati Rallapalli, Kevin Chan, Thomas La Porta

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Mobile devices collect a large amount of visual data that are useful for many applications. Searching for an object of interest over a network of mobile devices can aid human analysts in a variety of situations. However, processing the information on these devices is a challenge owing to the high computational complexity of the state-of-the-art computer vision algorithms that primarily rely on Convolutional Neural Networks (CNNs). Thus, this paper builds PicSys, a system that enables answering visual search queries on a mobile network. The objective of the system is to minimize the maximum completion time over all devices while taking into account the energy consumption of mobile devices as well. First, PicSys carefully divides the computation into multiple filtering stages, such that only a small percentage of images need to run the entire CNN pipeline. Splitting such CNN computation into multiple stages requires understanding the intermediate CNN features and systematically trading off accuracy for the computation speed. Second, PicSys determines where to run each of the stages of the multi-stage pipeline to fully utilize the available resources. Finally, through extensive experimentation, system implementation, and simulation, we show that PicSys performance is close to optimal and significantly outperforms other standard algorithms.

Original languageEnglish
Article number8946323
Pages (from-to)1574-1589
Number of pages16
JournalIEEE Transactions on Mobile Computing
Issue number4
StatePublished - Apr 1 2021

Bibliographical note

Funding Information:
This work was supported by the Army Research Laboratory and accomplished under Cooperative Agreement Number W911NF-09-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

Publisher Copyright:
© 2002-2012 IEEE.


  • Distributed systems
  • crowdsourcing
  • deep learning
  • energy

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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