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Record W2546158081 · doi:10.1109/icm.2008.5393838

Gate level static power estimation in UDSM processes

2008· article· en· W2546158081 on OpenAlexaff
Hussam Al-Hertani, D. Al-Khalili, C. Rozon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsSpiceLogic gateTransistorComputer scienceElectronic engineeringDissipationEstimatorPass transistor logicLeakage (economics)OR gatePower (physics)AND gateAlgorithmElectrical engineeringEngineeringMathematicsVoltagePhysics

Abstract

fetched live from OpenAlex

This paper introduces a new approach to estimating static leakage current, which leads to the calculation of static power dissipation in basic and complex logic gates. The approach utilizes a transistor collapsing scheme which merges pull-up/down networks into transistor stacks. Leakage current in these stacks can then be easily estimated using a stack estimator proposed by Hertani, et al.. The proposed approach is highly analytical (at the logic gate level), therefore exhibiting high computational efficiency as well as good accuracy. Compared to SPICE simulations, the average percentage errors ranges from 0.1-5.4% for basic logic gates and 3.7-6.2% for complex gates across the 32 nm, 45 nm and 65 nm PTM technologies.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.519

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 head0.024
GPT teacher head0.215
Teacher spread0.191 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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