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Record W4233542360 · doi:10.1109/dac.1992.227836

Generalized moment-matching methods for transient analysis of interconnect networks

2003· article· en· W4233542360 on OpenAlex
Eli Chiprout, M. Nakhla

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

Venue[1992] Proceedings 29th ACM/IEEE Design Automation Conference · 2003
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsCarleton University
Fundersnot available
KeywordsMoment (physics)WaveformTransient (computer programming)Matching (statistics)Lossy compressionComputer scienceStability (learning theory)Nonlinear systemInterconnectionAlgorithmSet (abstract data type)Electronic engineeringMathematicsEngineeringArtificial intelligenceTelecommunicationsPhysicsMachine learning

Abstract

fetched live from OpenAlex

An approach is introduced which improves published moment matching methods used in transient waveform estimation of large linear networks including lossy, coupled transmission lines. The method, which selects from a general set of moment-matching approximations, ensures stability while increasing the accuracy of the transient response. The technique is useful for analysis of high-speed interconnects including lumped and distributed linear components with nonlinear terminations. Examples are presented which demonstrate the stability and accuracy of the new method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.046
GPT teacher head0.305
Teacher spread0.259 · 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