MétaCan
Menu
Back to cohort

The Improvement of Pseudo-Boolean Satisfiability Algorithm for FPGA Routing

2012· article· en· W2028739735 on OpenAlexaff
Yu Tang, Jian Hui Chen

Bibliographic record

VenueAdvanced materials research · 2012
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsLa Cité Collégiale
Fundersnot available
KeywordsRouting (electronic design automation)Boolean satisfiability problemComputer scienceAlgorithmField-programmable gate arraySatisfiabilityEmbedded system

Abstract

fetched live from OpenAlex

A new routing algorithm is proposed for FPGA to improve the increasing transformation cost of pseudo-Boolean Satisfiability algorithm in the routing process, which combined advantages of pseudo-Boolean Satisfiability and geometric routing algorithm. In the routing process, one of geometric routing algorithm-VPR5.0 was chosen firstly for FPGA routing. If not successful, then use pseudo-Boolean Satisfiability algorithm. Technique of static symmetry-breaking is also adding to carry out pretreatment of pseudo-Boolean constraints, detecting and breaking the symmetries in the routing flow. The purpose was to prune search path, and the cost was consequently reduced. Preliminary experiments results show that the hybrid approach can reduce the runtime observably, speed up the solving process, and have no adverse affect on overall program.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.036
GPT teacher head0.348
Teacher spread0.313 · 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 designBench or experimental
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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced materials researchSame topicVLSI and FPGA Design TechniquesFrench-language works237,207