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Record W2162180547 · doi:10.1109/iscas.2005.1466066

Fast Integer Linear Programming Based Models for VLSI Global Routing

2005· article· en· W2162180547 on OpenAlexafffund
Laleh Behjat, Andy Chiang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRouterInteger programmingComputer scienceRouting (electronic design automation)Very-large-scale integrationMathematical optimizationPruningTree (set theory)MinificationLinear programmingGlobal optimizationParallel computingAlgorithmMathematicsComputer networkEmbedded system

Abstract

fetched live from OpenAlex

Global routing is an essential part of VLSI physical design, and has been traditionally solved using sequential or concurrent methods. In the sequential techniques, routes are generated one at a time based on a predetermined ordering. These methods are very fast, but because of their sequential nature can result in sub-optimal solutions. Concurrent techniques attempt to solve the problem using global optimization techniques. These methods can provide a global view of the circuit's routing, but take a considerable amount of time. A global router based on concurrent techniques is presented. The proposed technique formulates the global routing problem as an integer linear programming (ILP) problem. This model combines the traditional wire length minimization model with channel capacity minimization to obtain more accurate routings. In addition, the characteristics of the trees generated by our global router are investigated. A tree pruning technique, based on the characteristics of the trees, is developed to reduce the size of the ILP problem, and consequently reduce the solution time. The results show an average of 58% improvement in solving time without any loss in the quality of the results.

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: Methods · Consensus signal: none
Teacher disagreement score0.699
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 head0.019
GPT teacher head0.251
Teacher spread0.232 · 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
GenreMethods

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

Citations14
Published2005
Admission routes2
Has abstractyes

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