MultiDroid: An Energy Optimization Technique for Multi-Window Operations on OLED Smartphones
Bibliographic record
Abstract
The inbuilt multi-window feature released with Android Nougat has enabled simultaneous working and visualization of multiple applications on mobile display screen. However, multi-window operations result in unnecessary energy drain due to increased multitasking, CPU load, and multithread processing. Considering linear dependence between power consumed and the displayed colors, we present the design and realization of MultiDroid, a novel display power reduction technique during multi-window operations for the OLED screens. MultiDroid employs dynamic local dimming based on the context switching between the application pairs displayed on the screen. The display optimization works based on the user interaction with the mobile screen, where dynamic changes on the display screens are implemented for the non-critical application window. MultiDroid architecture is validated through power modeling and correctness verification. Furthermore, a survey on multi-window usage has been conducted reflecting the application usage pattern. The performance of MultiDroid is evaluated on preferred application pairs on multi-window framework obtained from the survey. Modeling and comparative analysis of the energy profiles for the devised test cases on optimized and default multi-window screens reflect around 10% to 25% reduction in the overall device power consumption per hour with a negligible performance degradation. After experimental validation, we present user acceptance and feasibility of MultiDroid based on the feedback from 50 participants.
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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 | 0.001 |
| 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".