Adaptive timeout policies for fast fine-grained power management
Bibliographic record
Abstract
Power management techniques for mobile appliances put the components of the systems into low power states to maximize battery life while minimizing the impact on the perceived per-formance of the devices. Static timeout policies are the state-of-the-art approach for solving power management problems. In this work, we propose adaptive timeout policies as a simple and efficient solution for fine-grained power management. As discussed in the paper, the policies reduce the latency of static timeout policies by nearly one half at the same power savings. This result can be also viewed as increasing the power savings of static timeout policies at the same latency target. The main objective of our work is to propose practical adaptive policies. Therefore, our adaptive solution is fast enough to be executed within less than one millisecond, and sufficiently simple to be deployed directly on a microcontroller. We validate our ideas on two recorded CPU activity traces, which involve more than 10 million entries each.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".