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While the study of energy consumption in the field of computer architecture remains widely studied, it has received less attention in the field of machine learning and artificial intelligence. Artificial Intelligence and Machine Learning models are widely utilised in various applications including data science, computer vision and natural language processing. Despite being a highly incentivised and sought-after field, most of its research is concentrated on the size of the models, amount of data and accuracies without concern for computational constraints such as power and energy consumption. This partially stems from a limited availability of energy evaluating tools in machine learning and lack of support from frameworks and cloud providers, largely due to security concerns. This research evaluates energy consumption and carbon emissions of several machine learning and deep learning models in various use cases. This study uses existing energy-measuring tools to provide insights into sustainable choices of models for lightweight applications.

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Energy Consumption for Training and Inference of Machine Learning Models and Their Processes

  • 章节

  • 发表日期：

    2025年

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  • Muhammad Mursalin Larik,
  • Ah-Lian Kor,
  • Nawar Jawad

摘要

While the study of energy consumption in the field of computer architecture remains widely studied, it has received less attention in the field of machine learning and artificial intelligence. Artificial Intelligence and Machine Learning models are widely utilised in various applications including data science, computer vision and natural language processing. Despite being a highly incentivised and sought-after field, most of its research is concentrated on the size of the models, amount of data and accuracies without concern for computational constraints such as power and energy consumption. This partially stems from a limited availability of energy evaluating tools in machine learning and lack of support from frameworks and cloud providers, largely due to security concerns. This research evaluates energy consumption and carbon emissions of several machine learning and deep learning models in various use cases. This study uses existing energy-measuring tools to provide insights into sustainable choices of models for lightweight applications.