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Record W2114817142 · doi:10.5555/996070.1009975

Statistical Verification of Power Grids Considering Process-Induced Leakage Current Variations

2003· article· en· W2114817142 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVoltage dropThreshold voltageMonte Carlo methodTransistorVoltageGridElectronic engineeringScalingComputer scienceExponential functionLog-normal distributionLeakage (economics)Electrical engineeringMathematicsEngineeringStatistics

Abstract

fetched live from OpenAlex

Transistor threshold voltages (V th ) have been reduced as part of on-going technology scaling. The smaller V th values feature increased variations due to underlying process variations, with a strong within-die component. Correspondingly, given the exponential dependence of leakage on V th , circuit leakage currents are increasing significantly and have strong within-die statistical variations. With these leakage currents loading the power grid, the grid develops correspondingly large statistical voltage drops. This leakage-induced voltage drop is an unavoidable background level of noise on the grid. Any additional non-leakage currents due to circuit activity will lead to voltage drop which is to be added to this background noise. We propose a technique for checking whether the statistical voltage drop on every node is within user-specified bounds, given user-specified statistics of the leakage currents.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.671
Threshold uncertainty score0.555

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)0.0010.000

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 head0.018
GPT teacher head0.255
Teacher spread0.237 · 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

Citations38
Published2003
Admission routes1
Has abstractyes

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