A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core Mobile Processors
Bibliographic record
Abstract
Dynamic voltage and frequency scaling (DVFS) is commonly used for the balance of performance and power of mobile processors. Conventional DVFS regulators take the processor cores with the same power and performance weight showing their inability in the regulation of heterogeneous processors. This paper proposes a core-aware frequency-power evaluation scheme that utilizes a multilayer perceptron (MLP) to assess the efficacy of DVFS regulation actions. Additionally, a ResNet-based DVFS regulator, trained with the assistance of the MLP, is developed for heterogeneous multi-core processors. Evaluated on Xiaomi 13 Pro powered by a Qualcomm Snapdragon 8 Gen 2, the proposed DVFS approach achieves an up to ×1.47 improvement in FPS Performance Per Watt (FPPW) compared with the Qualcomm's default DVFS governor in video recording scenario.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".