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Record W4253940019 · doi:10.1109/aspdac.2004.1337579

Piecewise quadratic waveform matching with successive chord iteration

2004· article· en· W4253940019 on OpenAlexaff
Z. Wang, Jianwen Zhu

Bibliographic record

VenueASP-DAC 2004: Asia and South Pacific Design Automation Conference 2004 (IEEE Cat. No.04EX753) · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWaveformElectronic circuitSpiceNonlinear systemPiecewisePiecewise linear functionResistive touchscreenAlgorithmControl theory (sociology)Computer scienceMathematicsVoltageElectronic engineeringMathematical analysisPhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

While fast timing analysis methods based on model order reduction have been well established for linear circuits, the timing analysis for nonlinear circuits, which are dominant in digital circuits, is usually performed by a SPICE-like, numerical integration-based approach solving differential equations. We propose a new technique that leads to the transient solution of charge/discharge paths with a complexity equivalent to only K DC operating point calculations, where K is the number of transistors along the path. This is accomplished by approximating each nodal voltage as a piecewise quadratic waveform, whose characteristics can be determined by matching the charge/discharge currents calculated by the capacitive components and the resistive components. Successive chord method is then applied to reduce the matrix construction and inversion overhead. Experiments on a wide range of circuits show that an average of 20 times speed-up over HSPICE simulation (transient time only) with 10 picosecond step size can be achieved, while maintaining an average accuracy of 98.03%.

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 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 score0.738
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.019
GPT teacher head0.234
Teacher spread0.214 · 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.

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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

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