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Record W2124872750 · doi:10.1109/ccece.2009.5090130

Dynamic leakage aware power management with procrastination method

2009· article· en· W2124872750 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsSt. Francis Xavier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProcrastinationDynamic demandComputer scienceLeakage powerLeakage (economics)Power managementPower consumptionDynamic voltage scalingFrequency scalingEnergy consumptionVoltagePower (physics)Reliability engineeringScalingReal-time computingEngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

Power management is an important factor during the design of real-time systems. Dynamic voltage scaling is an effective technique to reduce dynamic power consumption. With the higher density of processors, the leakage power consumption can not be ignored, which makes power management more complicated. Power shutdown and procrastination method can be used to reduce static power consumption due to leakage current efficiently. A dynamic leakage aware power management algorithm with procrastination for job-level periodic task set using EDF policy is developed. Experimental results show that it derives a good energy performance.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.391

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 communication0.0000.001
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 head0.005
GPT teacher head0.257
Teacher spread0.252 · 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

Quick stats

Citations3
Published2009
Admission routes2
Has abstractyes

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