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On Developing Sustainable Deep Learning Applications Using Pre-calculating Energy Usage

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Smart Cities, Green Technologies, and Intelligent Transport Systems (SMARTGREENS 2022, VEHITS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1843))

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Abstract

Sustainable computing is essential to our modern digital society. It deals with how computing resources and devices can be developed and used to perform operations as efficiently and eco-friendly as possible. With the explosive use of Deep Learning (DL) application systems whose development is known to be computationally intensive, this paper investigates sustainable development of DL applications to exemplify other software development. While much research on sustainable hardware development has made good progresses to reduce electronic waste and power consumption, sustainable software development is relatively behind. Most aim to find energy-efficient solutions as a result from improving computational efficiency (e.g., via optimization). This is useful but not direct. Before one can develop sustainable software, it is necessary to be able to assess and measure energy usage of the software computation. This paper presents an analytical modeling approach to quantifying energy consumption and illustrates how it can help achieve sustainable software development. In particular, we develop an energy model, for DL application systems, that has been evaluated theoretically and empirically on real systems. Unlike most existing work, our approach provides the ability to pre-determine the required energy consumption of DL applications prior to system implementation. The paper illustrates how the approach can help sustainable development of DL application system for monitoring crop health in smart agriculture in two scenarios: 1) when scaling the DL applications based on energy consumed by various design choices, and 2) when deciding whether to use sensors or drones to expand monitoring coverage.

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Authors and Affiliations

  1. Texas Tech University, Lubbock, TX, 79409, USA

    Supadchaya Puangpontip & Rattikorn Hewett

Authors
  1. Supadchaya Puangpontip
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  2. Rattikorn Hewett
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Correspondence to Supadchaya Puangpontip .

Editor information

Editors and Affiliations

  1. Siemens AG, Munich, Germany

    Cornel Klein

  2. RWTH Aachen, Aachen, Germany

    Matthias Jarke

  3. Siemens Industry Software Netherlands B.V., Eindhoven, The Netherlands

    Jeroen Ploeg

  4. Maynooth University, Maynooth, Kildare, Ireland

    Markus Helfert

  5. University of Kaiserslautern, Kaiserslautern, Germany

    Karsten Berns

  6. Ford Motor Company, Commerce Township, MI, USA

    Oleg Gusikhin

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Puangpontip, S., Hewett, R. (2023). On Developing Sustainable Deep Learning Applications Using Pre-calculating Energy Usage. In: Klein, C., Jarke, M., Ploeg, J., Helfert, M., Berns, K., Gusikhin, O. (eds) Smart Cities, Green Technologies, and Intelligent Transport Systems. SMARTGREENS VEHITS 2022 2022. Communications in Computer and Information Science, vol 1843. Springer, Cham. https://doi.org/10.1007/978-3-031-37470-8_2

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  • DOI: https://doi.org/10.1007/978-3-031-37470-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37469-2

  • Online ISBN: 978-3-031-37470-8

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