Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments
<|reference_start|>Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments: Monitoring, understanding, and optimizing the energy consumption of Machine Learning (ML) are various reasons why it is necessary to evaluate the energy usage of ML. However, there exists no universal tool that can answer this question for all use cases, and there may even be disagreement on how to evaluate energy consumption for a specific use case. Tools and methods are based on different approaches, each with their own advantages and drawbacks, and they need to be mapped out and explained in order to select the most suitable one for a given situation. We address this challenge through two approaches. First, we conduct a systematic literature review of all tools and methods that permit to evaluate the energy consumption of ML (both at training and at inference), irrespective of whether they were originally designed for machine learning or general software. Second, we develop and use an experimental protocol to compare a selection of these tools and methods. The comparison is both qualitative and quantitative on a range of ML tasks of different nature (vision, language) and computational complexity. The systematic literature review serves as a comprehensive guide for understanding the array of tools and methods used in evaluating energy consumption of ML, for various use cases going from basic energy monitoring to consumption optimization. Two open-source repositories are provided for further exploration. The first one contains tools that can be used to replicate this work or extend the current review. The second repository houses the experimental protocol, allowing users to augment the protocol with new ML computing tasks and additional energy evaluation tools.<|reference_end|>
@article{rodriguez2024evaluating,
    title={Evaluating the Energy Consumption of Machine Learning: Systematic
  Literature Review and Experiments},
    author={Charlotte Rodriguez, Laura Degioanni, Laetitia Kameni, Richard Vidal,
  and Giovanni Neglia},
    journal={arXiv preprint arXiv:2408.15128},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15128},
    primaryClass={cs.LG cs.AI cs.CY}
}