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Record W2122200015 · doi:10.1109/fpt.2006.270311

Perturber: semi-synthetic circuit generation using ancestor control for testing incremental place and route

2006· article· en· W2122200015 on OpenAlexaff
David Grant, Guy Lemieux

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBenchmark (surveying)Computer scienceHeuristicRouting (electronic design automation)Key (lock)Electronic circuitField-programmable gate arrayPath (computing)Computer engineeringAlgorithmParallel computingComputer hardwareEmbedded systemEngineeringElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

FPGA architects are always searching for more benchmark circuits to stress CAD tools and device architectures. In this paper we present a new heuristic to generate benchmark circuits specifically for incremental place and route tools. The method removes part of a real circuit and replaces it with a modified version of the same circuit to mimic an incremental design change. The generation procedure exactly preserves key circuit characteristics and achieves a post-routing channel width, critical path, and wire length that closely approximates those of the original circuit. Additionally, the method is fast and thus is suitable for use in on-the-fly benchmark generation

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.527

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 communication0.0000.000
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 head0.063
GPT teacher head0.245
Teacher spread0.182 · 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.

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

Citations4
Published2006
Admission routes1
Has abstractyes

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