Enhancing Energy-Awareness in Deep Learning through Fine-Grained Energy Measurement
Why this work is in the frame
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Bibliographic record
Abstract
With the increasing usage, scale, and complexity of Deep Learning ( dl ) models, their rapidly growing energy consumption has become a critical concern. Promoting green development and energy awareness at different granularities is the need of the hour to limit carbon emissions of dl systems. However, the lack of standard and repeatable tools to accurately measure and optimize energy consumption at fine granularity (e.g., at the api level) hinders progress in this area. This paper introduces FECoM (Fine-grained Energy Consumption Meter) , a framework for fine-grained dl energy consumption measurement. FECoM enables researchers and developers to profile dl api s from energy perspective. FECoM addresses the challenges of fine-grained energy measurement using static instrumentation while considering factors such as computational load and temperature stability. We assess FECoM ’s capability for fine-grained energy measurement for one of the most popular open-source dl frameworks, namely TensorFlow . Using FECoM , we also investigate the impact of parameter size and execution time on energy consumption, enriching our understanding of TensorFlow api s’ energy profiles. Furthermore, we elaborate on the considerations and challenges while designing and implementing a fine-grained energy measurement tool. This work will facilitate further advances in dl energy measurement and the development of energy-aware practices for dl systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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