Summary of Evaluating the Energy Consumption Of Machine Learning: Systematic Literature Review and Experiments, by Charlotte Rodriguez et al.
Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments
by Charlotte Rodriguez, Laura Degioanni, Laetitia Kameni, Richard Vidal, Giovanni Neglia
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the crucial issue of evaluating the energy consumption of Machine Learning (ML) models. Despite the growing importance of monitoring and optimizing energy usage in ML, there is currently no universal tool that can be applied across all use cases. The existing tools and methods are diverse, with each having its own strengths and limitations. To address this challenge, the authors employ two approaches: a systematic literature review to map out the various tools and methods for evaluating energy consumption of ML, both at training and inference stages; and an experimental protocol to compare a selection of these tools and methods on a range of ML tasks with varying 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, while the experimental protocol provides a framework for users to extend or replicate the study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are getting more powerful, but they’re also using more energy! This paper looks at how we can measure and compare the energy use of different machine learning models. It’s like trying to find the best recipe – you need to know what ingredients (tools) you have, how they work together, and which one is the best for your specific dish (task). The authors do a big review of all the tools and methods people are using to measure energy consumption in machine learning, then they test some of them on different types of tasks. This helps us understand what’s out there and how we can use these tools to make our own models more efficient. |
Keywords
» Artificial intelligence » Inference » Machine learning
