Thermal vs Energy Optimization for DVFS-Enabled Processors in Embedded Systems
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".