Green Deep Learning: Sustainable Model Design through Data-Centric Optimization Techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increasing scale and complexity of Deep Learning (DL) models have led to substantial energy consumption, raising environmental and sustainability concerns. This paper explores green deep learning, a paradigm that integrates sustainability into the design and training of DL systems, through data-centric optimization techniques. The highlight strategies such as dataset pruning, data augmentation, label quality improvement, and smart sampling, which reduce computational overhead while maintaining or enhancing model performance. Case studies and experiments demonstrate how data-centric methods significantly improve training efficiency and reduce environmental impact. This study provides insights into future directions for designing energy-efficient, high-performance DL models aligned with global sustainability goals.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it