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Record W2001180691 · doi:10.1142/s0218126608004526

A FAST AND EFFECTIVE TIMING-DRIVEN PLACEMENT TOOL FOR FPGAs

2008· article· en· W2001180691 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

VenueJournal of Circuits Systems and Computers · 2008
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBenchmark (surveying)Field-programmable gate arrayRouting (electronic design automation)Computer scienceProcess (computing)Quadratic equationPath (computing)Electronic circuitMicroelectronicsChipAlgorithmMathematicsEmbedded systemEngineeringElectrical engineeringTelecommunicationsGeometry

Abstract

fetched live from OpenAlex

In this paper, we present TQPF, a Timing-Driven Quadratic-based Placement Tool for FPGAs. Quadratic placement algorithms try to minimize total squared wire length by solving linear equations. The resulting placement tends to locate all cells near the center of the chip with a large amount of overlap. Also, since squared wire length is only an indirect measure of linear wire length, the resulting total wire length may not be minimized. We propose methods to alleviate the above two problems that give high-quality results while minimizing the total run time. We incorporate multiple iterations of equation-solving process together with a technique for pulling nodes out of the dense area while minimizing linear wire length. Experimental results using 20 Microelectronics Center of North Carolina (MCNC) benchmark circuits show that, on average, TQPF is approximately three times faster than the well-known Versatile Placement and Routing tool for FPGAs (VPR). The estimated total wire length, on average, is only 1.4% longer, and the critical path delay is 4.9% lower.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score0.371

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 head0.015
GPT teacher head0.211
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