16764

Fast and Energy-Efficient CNN Inference on IoT Devices

Mohammad Motamedi, Daniel Fong, Soheil Ghiasi
ECE Department, University of California, Davis
arXiv:1611.07151 [cs.DC], (22 Nov 2016)

@article{motamedi2016fast,

   title={Fast and Energy-Efficient CNN Inference on IoT Devices},

   author={Motamedi, Mohammad and Fong, Daniel and Ghiasi, Soheil},

   year={2016},

   month={nov},

   archivePrefix={"arXiv"},

   primaryClass={cs.DC}

}

Convolutional Neural Networks (CNNs) exhibit remarkable performance in various machine learning tasks. As sensor-equipped internet of things (IoT) devices permeate into every aspect of modern life, it is increasingly important to run CNN inference, a computationally intensive application, on resource constrained devices. We present a technique for fast and energy-efficient CNN inference on mobile SoC platforms, which are projected to be a major player in the IoT space. We propose techniques for efficient parallelization of CNN inference targeting mobile GPUs, and explore the underlying tradeoffs. Experiments with running Squeezenet on three different mobile devices confirm the effectiveness of our approach. For further study, please refer to the project repository available on our GitHub page.
Rating: 1.9/5. From 4 votes.
Please wait...

* * *

* * *

Featured events

2018
November
27-30
Hida Takayama, Japan

The Third International Workshop on GPU Computing and AI (GCA), 2018

2018
September
19-21
Nagoya University, Japan

The 5th International Conference on Power and Energy Systems Engineering (CPESE), 2018

2018
September
22-24
MediaCityUK, Salford Quays, Greater Manchester, England

The 10th International Conference on Information Management and Engineering (ICIME), 2018

2018
August
21-23
No. 1037, Luoyu Road, Hongshan District, Wuhan, China

The 4th International Conference on Control Science and Systems Engineering (ICCSSE), 2018

2018
October
29-31
Nanyang Executive Centre in Nanyang Technological University, Singapore

The 2018 International Conference on Cloud Computing and Internet of Things (CCIOT’18), 2018

HGPU group © 2010-2018 hgpu.org

All rights belong to the respective authors

Contact us: