A Framework for Learning Based DVFS Technique Selection and Frequency Scaling for Multi-core Real-Time Systems
Bibliographic record
Abstract
Multi-core processors have become very popular in recent years due to the higher throughput and lower energy consumption compared with unicore processors. They are widely used in portable devices and real-time systems. Despite of enormous prospective, limited battery capacity restricts their potential and hence, improving the system level energy management is still a major research area. In order to reduce the energy consumption, dynamic voltage and frequency scaling (DVFS) has been commonly used in modern processors. Previously, we have used reinforcement learning to scale voltage and frequency based on the task execution characteristics.We have also designed learning based method to choose a suitable DVFS technique to execute at different states. In this paper, we propose a generalized framework which integrates these two approaches for real-time systems on multi-core processors. The framework is generalized in a sense that it can work with different scheduling policies and existing DVFS techniques.
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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 | 0.001 | 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".