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Record W1993072227 · doi:10.5555/1015090.1015284

Interconnect capacitance estimation for FPGAs

2004· article· en· W1993072227 on OpenAlexaff
Jason H. Anderson, Farid N. Najm

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCapacitanceField-programmable gate arrayRouting (electronic design automation)Noise (video)Dynamic demandElectronic engineeringComputer scienceCMOSBounding overwatchInterconnectionPower (physics)Lookup tableParasitic capacitanceMinimum bounding boxEngineeringEmbedded systemTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

The dynamic power consumed by a digital CMOS circuit is directly proportional to capacitance. In this paper, we consider pre-routing capacitance estimation for FPGAs and develop an empirical estimation model, suitable for use in power-aware placement, early power prediction, and other applications. We show that estimation accuracy is improved by considering aspects of the FPGA interconnect architecture in addition to generic parameters, such as net fanout and bounding box perimeter length. We also show that there is an inherent variability (noise) in the capacitance of nets routed using a commercial FPGA layout tool. This variability limits the accuracy attainable in capacitance estimation. Experimental results show that the proposed estimation model works well given the noise limitations.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score0.197

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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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
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

Citations11
Published2004
Admission routes1
Has abstractyes

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