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Record W2047363496 · doi:10.1109/tvlsi.2015.2413966

Verification of the Power and Ground Grids Under General and Hierarchical Constraints

2015· article· en· W2047363496 on OpenAlexafffund
Mehmet Avci, Farid N. Najm

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaIntel CorporationSemiconductor Research Corporation
KeywordsComputer sciencePower (physics)GridVoltageRange (aeronautics)Design flowElectronic engineeringComputer engineeringTopology (electrical circuits)Electrical engineeringEmbedded systemMathematicsEngineering

Abstract

fetched live from OpenAlex

As part of power distribution network verification, one should check if the voltage fluctuations exceed some critical threshold. The traditional simulation-based solution to this problem is intractable due to the large number of possible circuit behaviors. This approach also requires full knowledge of the details of the underlying circuitry, not allowing one to verify the power distribution network early in the design flow. Contrary to previous work on power distribution network verification, we consider the power and ground (P/G) grids together and describe an early verification approach under the framework of current constraints. Then, we present a solution technique in which tight lower and upper bounds on worst case voltage fluctuations are computed via linear programs. Experimental results indicate that the proposed technique results in errors in the range of a few millivolts. In addition to P/G grid verification techniques, we also provide very efficient solution technique to power (single) grid verification under hierarchical current constraints.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2015
Admission routes2
Has abstractyes

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