Modeling the resource requirements of convolutional neural networks on mobile devices

Zongqing Lu, Swati Rallapalli, Kevin Chan, Thomas La Porta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Convolutional Neural Networks (CNNs) have revolutionized the research in computer vision, due to their ability to capture complex patterns, resulting in high inference accuracies. However, the increasingly complex nature of these neural networks means that they are particularly suited for server computers with powerful GPUs. We envision that deep learning applications will be eventually and widely deployed on mobile devices, e.g., smartphones, self-driving cars, and drones. Therefore, in this paper, we aim to understand the resource requirements (time, memory) of CNNs on mobile devices. First, by deploying several popular CNNs on mobile CPUs and GPUs, we measure and analyze the performance and resource usage for every layer of the CNNs. Our findings point out the potential ways of optimizing the performance on mobile devices. Second, we model the resource requirements of the different CNN computations. Finally, based on the measurement, profiling, and modeling, we build and evaluate our modeling tool, Augur, which takes a CNN configuration (descriptor) as the input and estimates the compute time and resource usage of the CNN, to give insights about whether and how efficiently a CNN can be run on a given mobile platform. In doing so Augur tackles several challenges: (i) how to overcome profiling and measurement overhead; (ii) how to capture the variance in different mobile platforms with different processors, memory, and cache sizes; and (iii) how to account for the variance in the number, type and size of layers of the different CNN configurations.

Original languageEnglish (US)
Title of host publicationMM 2017 - Proceedings of the 2017 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages1663-1671
Number of pages9
ISBN (Electronic)9781450349062
DOIs
StatePublished - Oct 23 2017
Event25th ACM International Conference on Multimedia, MM 2017 - Mountain View, United States
Duration: Oct 23 2017Oct 27 2017

Publication series

NameMM 2017 - Proceedings of the 2017 ACM Multimedia Conference

Other

Other25th ACM International Conference on Multimedia, MM 2017
CountryUnited States
CityMountain View
Period10/23/1710/27/17

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Software

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