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Record W2147209308 · doi:10.1109/asic.2001.954706

A clustering utility based approach for ASIC design

2002· article· en· W2147209308 on OpenAlexaff
Shawki Areibi, Michael Thompson, Anthony Vannelli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of WaterlooUniversity of Guelph
Fundersnot available
KeywordsApplication-specific integrated circuitCluster analysisComputer scienceHeuristicInterconnectionElectronicsKey (lock)ChipSystem on a chipEmbedded systemIntegrated circuitIntegrated circuit designComputer engineeringComputer architectureEngineeringElectrical engineeringArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

Due to the rapid growth of technologies, systems-on-chip (SoC) have started to become a key issue in today's electronics industry. In deep sub-micron designs, the interconnect is responsible for more than 90 percent of the signal delay in a chip. This paper presents a new approach for dealing with the high complexity of ASIC design. A new hierarchal clustering heuristic is presented that demonstrates excellent characteristics for reducing the execution time of standard-cell placement while achieving better results compared to non-clustered circuit placement methods. The clustering algorithm reduced the wire-length by 2% for small circuits and up to 10% for large circuits. Total execution time was reduced by more than 70% as expected.

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 score0.830
Threshold uncertainty score0.404

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 head0.080
GPT teacher head0.222
Teacher spread0.142 · 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

Citations7
Published2002
Admission routes1
Has abstractyes

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