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Record W4250192995 · doi:10.1109/pads.2003.1207433

DVS: an object-oriented framework for distributed Verilog simulation

2004· article· en· W4250192995 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceVerilogCorrectnessLogic simulationBottleneckComputer architectureDistributed computingHardware description languageWorkstationProcessor designTime to marketEmbedded systemParallel computingField-programmable gate arrayLogic gateOperating systemProgramming language

Abstract

fetched live from OpenAlex

There is a wide spread usage of hardware design languages (HDL) to speed up the time to market for the design of modern digital systems. Verification engineers can simulate hardware in order to verify its performance and correctness with help of an HDL. However, simulation can't keep pace with the growth in size and complexity of circuits and has become a bottleneck of the design process. Distributed HDL simulation on a cluster of workstations has the potential to provide a cost effective solution to this problem. We describe the design and implementation of DVS, an object-oriented framework for distributed Verilog simulation. Verilog is an HDL which sees wide industrial use. DVS is an outgrowth of clustered time warp, originally developed for logic simulation. The design of the framework emphasizes simplicity and extensibility and aims to accommodate experiments involving partitioning and dynamic load balancing. Preliminary results obtained by simulating a 16 bit multiplier are presented.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.558
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.029
GPT teacher head0.326
Teacher spread0.296 · 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

Quick stats

Citations28
Published2004
Admission routes1
Has abstractyes

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