Summary of Normalizing Energy Consumption For Hardware-independent Evaluation, by Constance Douwes et al.
Normalizing Energy Consumption for Hardware-Independent Evaluation
by Constance Douwes, Romain Serizel
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a novel approach to normalizing energy consumption for machine learning (ML) models across different hardware platforms. The authors evaluate various normalization strategies by measuring the energy used to train different ML architectures on different GPUs for audio tagging tasks. They find that selecting two reference points and incorporating computational metrics improves the accuracy of energy consumption predictions, promoting environmentally sustainable ML practices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study shows how to normalize energy consumption for machine learning models so they’re fair and consistent across different devices. It’s about making sure we don’t waste energy when training AI models on our computers or phones. The researchers tested different ways to do this and found that using two reference points and counting things like floating-point operations helps get the most accurate results. |
Keywords
» Artificial intelligence » Machine learning
