Can Learning-Based Hybrid DVFS Technique Adapt to Different Linux Embedded Platforms?
Bibliographic record
Abstract
With the increased use of embedded platforms powered by Linux, battery life has become one of the central focuses in research and innovation. Towards this, dynamic voltage and frequency scaling (DVFS) has emerged as a powerful technique. However, reinforcement learning-based hybrid DVFS techniques for Mixed Task sets are hardly investigated, considering Linux operating systems targeted on embedded platforms. This paper illustrates an experimental analysis of an energy reduction technique using hybrid learning-based DVFS methodology for Mixed Task sets on Linux Embedded Platforms with ARM and Intel architectures. We analyze the hybrid DVFS approach using reinforcement learning to handle a Mixed Task set on Linux platforms and evaluate the algorithms on various real-time devices. Results show that we can successfully apply the extended hybrid DVFS technique on real-time Linux embedded platforms, i.e., Dell Laptop, Raspberry Pi, and Beagle Bone Platform, all focusing on energy saving. Over 3% energy savings can be achieved for a standard real-time scheduling mechanism without any penalty in application throughput. We believe that our implementation might potentially lead to a number of innovative applications that use Learning-based DVFS algorithms targeted on Linux real embedded hardware.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".