ENERGY EFFICIENCY ENHANCEMENT FOR CNN-BASED DEEP MOBILE SENSING

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number8752530
Pages (from-to)161-167
Journal / PublicationIEEE Wireless Communications
Volume26
Issue number3
Publication statusPublished - Jun 2019

Abstract

Recently, deep learning has been used to tackle mobile sensing problems, and the inference phase of deep learning is preferred to be run on mobile devices for speedy responses. However, mobile devices are resource-constrained platforms for both computation and power. Moreover, an inference task with deep learning involves tens of billions of mathematical operations and tens of millions of parameter reads. Thus, it is a critical issue to reduce the energy consumption of deep learning inference algorithms. In this article, we survey various energy reduction approaches, and classify them into three categories: the compressing neural network model, minimizing the data transfer required in computation, and offloading workloads. Moreover, we simulate and compare three techniques of model compression, by applying them to an object recognition problem.

Citation Format(s)

ENERGY EFFICIENCY ENHANCEMENT FOR CNN-BASED DEEP MOBILE SENSING. / Xie, Ruitao; Jia, Xiaohua; Wang, Lu; Wu, Kaishun.

In: IEEE Wireless Communications, Vol. 26, No. 3, 8752530, 06.2019, p. 161-167.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review