Focus on Carbon Dioxide Footprint of AI/ML Model Training
Bibliographic record
Abstract
In the last decade, AI/ML has accomplished impressive achievements in various fields. Although this increasing usage of AI/ML systems eases our daily life in different perspectives, it may have side effects. The latter includes the various consequences of high computational cost for training and testing such models. In some cases, these computations leave a considerable amount of carbon footprint without significantly improving the model accuracy over consecutive training iterations. As a result, negative impacts of AI/ML model training/testing should be taken into consideration when developing such systems. In this paper, we analyse the trade-off between carbon foot-print and AI/ML model accuracy using four different model classes: CNN, RNN, GRU and LSTM. Our results show that the accuracy achieved by training the model is not in line with the amount of carbon dioxide emitted. Besides, the carbon footprint patterns cannot be generalized for the four classes of tested models. We observe that the model with the best training accuracy does not generate the highest carbon footprint. We conclude with guidelines for training efficient AI/ML systems that also incorporate carbon footprint emission for a sustainable approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".