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Record W2135523993 · doi:10.5555/1326073.1326122

A performance-driven QBF-based iterative logic array representation with applications to verification, debug and test

2007· article· en· W2135523993 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
TopicFormal Methods in Verification
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceVery-large-scale integrationDebuggingEncoding (memory)Representation (politics)SatisfiabilityFrame (networking)Sequential logicModel checkingAlgorithmTheoretical computer scienceComputer engineeringParallel computingLogic gateProgramming languageEmbedded systemArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract — Many CAD for VLSI techniques use time-frame expansion, also known as the Iterative Logic Array representation, to model the sequential behavior of a system. Replicating industrialsize designs for many time-frames may impose impractically excessive memory requirements. This work proposes a performancedriven, succinct and parametrizable Quantified Boolean Formula (QBF) satisfiability encoding and its hardware implementation for modeling sequential circuit behavior. This encoding is then applied to three notable CAD problems, namely Bounded Model Checking (BMC), sequential test generation and design debugging. Extensive experiments on industrial circuits confirm outstanding run-time and memory gains compared to state-of-the-art techniques, promoting the use of QBF in CAD for VLSI. I.

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: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.423
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.321
Teacher spread0.284 · 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

Citations45
Published2007
Admission routes1
Has abstractyes

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