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Leveraging Banking and Charge-Sharing schemes for Low-Power Micro-displays

2024· article· en· W4402474990 on OpenAlexaff
Shubham Ranjan, Sheida Gohardehi, Manoj Sachdev

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGreen IT and Sustainability
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer sciencePower (physics)Charge (physics)Charge sharingElectrical engineeringTelecommunicationsEngineeringPhysics

Abstract

fetched live from OpenAlex

Micro-displays are often used in augmented reality, virtual reality applications, wearable devices, and consumer electronics. Given that these devices typically rely on batteries for power, it is crucial to reduce the power consumption in micro-displays, which is one of the most power-consuming components. In this article, we propose a banking scheme to reduce the effective capacitance of column-line and therefore, reduce the display backplane power consumption. Furthermore, we reduce the power consumption of row drivers by using the charge-sharing method. According to simulation results of the VGA display using TSMC 65nm technology, the dynamic power of the display backplane is reduced by 17.2% compared to the conventional display backplane.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.224
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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