Exploring the capabilities of mobile devices supporting deep learning

Yitao Chen, Saman Biookaghazadeh, Ming Zhao

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

3 Scopus citations

Abstract

With the increasingly more powerful mobile devices, it becomes possible to perform more deep learning tasks on the devices, and there are also important advantages of learning on devices, such as personalization and efficiency. However, a good understanding of the capabilities of modern mobile devices for deep learning is generally lacking. To address this gap in knowledge, this paper presents a comprehensive study on performing training and inference of deep neural networks (DNNs) on mobile devices. This study is based on TensorFlow+, an extension of the widely used TensorFlow framework that enables it to train DNNs on devices and use the available GPUs to accelerate the learning. The most significant results of our study are: 1) The size of the network is crucial not only to meet the device’s memory constraint but also for training performance; 2) Hardware acceleration is important to the learning speed on devices. By accelerating both the forward and backward path with the device’s GPU, our extended TensorFlow can cut down the training time by 44.8%; 3) Comparing CPU, memory, and battery usages, memory size is the most serious constraint to training networks on devices.

Original languageEnglish (US)
Title of host publicationHPDC 2018 - Proceedings of The 27th International Symposium on High-Performance Parallel and Distributed Computing Posters/Doctoral Consortium
PublisherAssociation for Computing Machinery, Inc
Pages17-18
Number of pages2
ISBN (Electronic)9781450358996
DOIs
StatePublished - Jun 11 2018
Event27th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2018 - Tempe, United States
Duration: Jun 11 2018 → …

Other

Other27th ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2018
Country/TerritoryUnited States
CityTempe
Period6/11/18 → …

Keywords

  • Deep learning
  • Edge computing
  • Neural networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computational Theory and Mathematics

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