Thermal vs Energy Optimization for DVFS-Enabled Processors in Embedded Systems
Why this work is in the frame
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Bibliographic record
Abstract
In the past, dynamic voltage and frequency scaling (DVFS) has been widely used for power and energy optimization in embedded system design. As thermal issues become increasingly prominent, we propose design-time thermal optimization techniques for embedded systems. By carefully planning DVFS at design time, our techniques proactively optimize system thermal profile, prevent run-time thermal emergencies, minimize cooling costs, and optimize system performance. To the best of our knowledge, this is the first work addressing embedded system design-time thermal optimization using DVFS. We formulate minimization of application peak temperature in the presence of real-time constraints as a nonlinear programming problem. This provides a powerful framework for system designers to determine a proper thermal solution and provide a lower bound on the minimum temperature achievable by DVFS. Furthermore, we examine the differences between optimal energy solutions and optimal peak temperature solutions. Experimental results indicate that optimizing energy consumption can lead to unnecessarily high temperature. Finally, we propose a thermal-constrained energy optimization procedure to minimize system energy consumption under a constraint on peak temperature
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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