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Record W2132929361 · doi:10.1109/pacrim.1993.407311

Modeling data transfer signal timings in DAME

2002· article· en· W2132929361 on OpenAlexaff
Benjamin Huber, K. Li, N.J. Dimopoulos, M.A. Escalante, Eric G. Manning

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInterface (matter)Computer scienceMicroprocessorAbstractionSIGNAL (programming language)Transfer (computing)AutomationStatic timing analysisElectronic design automationEmbedded systemComputer hardwareReal-time computingProgramming languageOperating systemEngineering

Abstract

fetched live from OpenAlex

The timing interface design subsystem in DAME (Design Automation of Microprocessor based systems, using an Expert system approach) is discussed. The timing design subsystem ensures that all signals produced by the interface between components meet the required timing specifications. A main goal of the timing and signal model is the abstraction of common features found in the data transfer timings of most components. This allows relatively few common rules to be used for the interface design which apply to all components. The authors introduce the timing and signal model used to specify the data transfer timing of microprocessor components. Examples of the different timing protocol templates are given, and the design strategy used by the timing design subsystem is discussed.>

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)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.140
GPT teacher head0.279
Teacher spread0.140 · 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
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

Citations2
Published2002
Admission routes1
Has abstractyes

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