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Record W2151614223 · doi:10.1109/fpl.2007.4380635

Improving Timing-Driven FPGA Packing with Physical Information

2007· article· en· W2151614223 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 Waterloo
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceCluster analysisPath (computing)PlacementCritical path methodParallel computingComputer engineeringEmbedded systemPhysical designComputer networkArtificial intelligenceEngineeringCircuit design

Abstract

fetched live from OpenAlex

The traditional approach to FPGA packing and CLB-level placement has been shown to yield significantly worse quality than approaches which allow BLEs to move during placement. In practice, however, modern FPGA architectures require expensive DRC checks which can render full BLE-level placement impractical. We address this problem by proposing a novel clustering framework that uses physical information to produce better initial packings which can, in turn, reduce the amount of BLE-level placement that is required. We quantify our packing technique across accepted benchmarks and show that it produces results with 16% less wire length, 19% smaller minimum channel widths, and 8% less critical path delay, on average, than leading methods.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.265

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 head0.007
GPT teacher head0.202
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

Citations47
Published2007
Admission routes1
Has abstractyes

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