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Cited by 236Parallel Computing and Optimization TechniquesAdvanced Neural Network ApplicationsEnergy Efficiency in Computing

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Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs

Li, Da, Chen, Xinbo, Becchi, Michela, Zong, Ziliang·2016·2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom)
DOI 10.1109/bdcloud-socialcom-sustaincom.2016.76
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Abstract

In recent years convolutional neural networks (CNNs) have been successfully applied to various applications that are appropriate for deep learning, from image and video processing to speech recognition. The advancements in both hardware (e.g. more powerful GPUs) and software (e.g. deep learning models, open-source frameworks and supporting libraries) have significantly improved the accuracy and training time of CNNs. However, the high speed and accuracy are at the cost of energy consumption, which has been largely ignored in previous CNN design. With the size of data sets grows exponentially, the energy demand for training such data sets increases rapidly. It is highly desirable to design deep learning frameworks and algorithms that are both accurate and energy efficient. In this paper, we conduct a comprehensive study on the power behavior and energy efficiency of numerous well-known CNNs and training frameworks on CPUs and GPUs, and we provide a detailed workload characterization to facilitate the design of energy efficient deep learning solutions.

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References 32 · Cited by 236

Research works printed but not linked: 24

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Metadata

Authors
Li, D., Chen, X., Becchi, M., & Zong, Z.
Affiliations
  • University of Missouri (US)
  • Texas State University (US)
  • University of Missouri System (US)
ORCID
Venue
2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom)
Publisher
IEEE
Published
2016-10
Pages
477-484
DOI
10.1109/bdcloud-socialcom-sustaincom.2016.76
OpenAlex
W2541249978
Cited by
236
References
32
Citations by year
2026: 10 · 2025: 28 · 2024: 29 · 2023: 70 · 2022: 34 · 2021: 25 · 2020: 27 · 2019: 7 · 2018: 4 · 2017: 2
FWCI
3.34
Type
conference paper
Language
en
Open access
closed
Topics
  • Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Keywords
energy efficiency, deep convolutional neural networks, power consumption, GPU training, workload characterization, energy-efficient deep learning

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