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Record W4236875804 · doi:10.1109/iccad.1996.571342

Directional bias and non-uniformity in FPGA global routing architectures

2002· article· en· W4236875804 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRouting (electronic design automation)PlacementComputer scienceChannel (broadcasting)Key (lock)Field-programmable gate arrayHorizontal and verticalChipTopology (electrical circuits)Electronic engineeringParallel computingComputer architectureElectrical engineeringComputer networkComputer hardwareTelecommunicationsEmbedded systemEngineeringPhysical designMathematicsGeometryCircuit design

Abstract

fetched live from OpenAlex

We investigate the effect of the prefabricated routing track distribution on the area-efficiency of FPGAs. The first question we address is whether horizontal and vertical channels should contain the same number of tracks (capacity), or if there is a density advantage with a directional bias. Secondly, should the channels have a uniform capacity, or is there an advantage when capacities vary from channel to channel? The key result is that the most area-efficient global routing architecture is one with uniform (or very nearly uniform) channel capacities across the entire chip in both the horizontal and vertical directions. Several non-uniform and directionally-biased architectures, however are fairly area-efficient provided that appropriate choices are made for the pin positions on the logic blocks and the logic array aspect ratio.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.306

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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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

Quick stats

Citations43
Published2002
Admission routes1
Has abstractyes

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