How Is Energy Consumed in Smartphone Deep Learning Apps? Executing Locally vs. Remotely

By Haoxin Wang, Baekgyu Kim, Jiang Xie, Zhu Han. 2019

18 citations recorded by the source corpus.

Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications use deep learning because such technology is computation- and energy-intensive. This paper, to the best of our knowledge, presents the first detailed experimental study of the smartphone's energy consumption and the detection latency of executing deep Convolutional Neural Networks (CNN) optimized object detec- tion, either locally on the smartphone or remotely on an edge server. We experiment with a variety of smartphones, obtaining different levels of computation capacities, in order to ensure that we are not profiling a specific device. Our detailed measurements refine the energy analysis of smartphones and reveal some interesting perspectives regarding the energy consumption of executing the deep CNN optimized object detection. We believe that these findings will guide the design of energy efficient processing pipeline of the CNN optimized object detection.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Efficiency & Compression

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Sustainable AI: Environmental Implications, Challenges and Opportunities (2021, background): “We assumed a device power of 3W and a router power of 7.5W [115], [114].”