Micro-display backplane power reduction techniques: Column segmentation and row charge sharing
Bibliographic record
Abstract
Micro-displays are ubiquitous in portable electronics, ranging from consumer products to specialized industrial and medical devices including heads-up displays (HUDs), and augmented reality (AR) and virtual reality (VR) headsets. To increase the battery life of portable electronics, it is important to address power consumption in micro-displays, which is the most power-hungry block in these devices. With the improved power efficiency of display media, the power consumption of the display backplane is still high, especially while streaming video. Therefore, to reduce overall power consumption, it is important to reduce the power consumption of the display backplane. This work investigates the impact of column segmentation techniques and row charge sharing on reducing the power consumption of the display backplane. The measurement result of a VGA (480 × 640) micro-display implemented in TSMC 65 nm technology shows a 18.4% reduction in the average total power consumption of the display backplane using a dual-column driver with column segmentation architecture. • Study of various column segmentation architectures to reduce column driver power. • Use of row charge sharing mechanism to reduce power consumption of row driver. • Understanding of effect of image pattern on display backplane power consumption. • Understanding the effect of increase in display resolution or refresh rate on power consumption of backplane.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".