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Record W4251811355 · doi:10.1109/tcad.2005.852665

Analysis and verification of power grids considering process-induced leakage-current variations

2005· article· en· W4251811355 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

VenueIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2005
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVoltage dropMonte Carlo methodThreshold voltageTransistorVoltageGridScalingElectronic engineeringLog-normal distributionExponential functionLeakage (economics)Computer scienceLogarithmElectrical engineeringMathematicsEngineeringStatistics

Abstract

fetched live from OpenAlex

The ongoing trends in technology scaling imply a reduction in the transistor threshold voltage (V/sub th/). With smaller feature lengths and smaller parameters, variability becomes increasingly important, for ignoring it may lead to chip failure and assuming worst case renders almost any design nonachievable. This paper presents a methodology for the analysis and verification of the power grid of integrated circuits considering variations in leakage currents. These variations are large due to the exponential relation between leakage current and transistor threshold voltage and appear as random background noise on the nodes of the grid. We propose a lognormal distribution to model the grid voltage drops, derive bounds on the voltage-drop variances, and develop a numerical Monte Carlo method to estimate the variance of each node voltage on the grid. This model is used toward the solution of a statistical formulation of the power-grid-verification problem.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.025
GPT teacher head0.232
Teacher spread0.208 · 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