MétaCan
Menu
Back to cohort
Record W2134616364 · doi:10.1109/iccd.1994.331890

Area efficient synthesis of asynchronous interface circuits

2002· article· en· W2134616364 on OpenAlexafffund
Ruchir Puri, J. Gu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsAsynchronous communicationComputer scienceDivide and conquer algorithmsGraphElectronic circuitInterface (matter)High-level synthesisDigital electronicsTheoretical computer scienceComputer engineeringComputer architectureAlgorithmParallel computingEmbedded systemComputer network

Abstract

fetched live from OpenAlex

Asynchronous circuits are widely used in many real time applications such as digital communication and computer systems. The design of complex asynchronous interface circuits is a difficult and error-prone task. We present an area and time efficient synthesis algorithm for general signal transition graph (STG) specifications. It utilizes a divide-and-conquer approach to significantly reduce the number of design constraints. We present a BDD constraint satisfaction algorithm that exploits the don't cares for area efficient synthesis. Experimental results with a large number of practical signal transition graph benchmarks are presented. These results show that compared to the existing techniques, the divide-and-conquer BDD technique is capable of achieving an average of 20% reduction in implementation area.>

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.265
Teacher spread0.225 · 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

Citations8
Published2002
Admission routes2
Has abstractyes

Explore more

Same topicFormal Methods in VerificationFrench-language works237,207