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Record W2416121487

Academic Clustering and Placement Tools for Modern Field-Programmable Gate Array Architectures

2008· dissertation· en· W2416121487 on OpenAlexaboutno aff
Daniele Giuseppe Paladino

Bibliographic record

VenueTSpace (University of Toronto) · 2008
Typedissertation
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsnot available
Fundersnot available
KeywordsCluster analysisField-programmable gate arrayField (mathematics)Gate arrayComputer scienceComputer architectureProgrammable logic arrayEngineeringEmbedded systemArtificial intelligenceMathematics
DOInot available

Abstract

fetched live from OpenAlex

Academic Clustering and Placement Tools 
\nfor Modern Field-Programmable Gate Array Architectures
\nDaniele Giuseppe Paladino
\nMasters of Applied Science
\nGraduate Department of Electrical and Computer Engineering
\nUniversity of Toronto
\n2008
\n
\n
\nAbstract
\n
\nAcademic tools have been used in many research studies to investigate Field-Programmable Gate Array (FPGA) architecture, but these tools are not sufficiently flexible to represent modern commercial devices. This thesis describes two new tools, the Dynamic Clusterer (DC) and the Dynamic Placer (DP) that perform the clustering and placement steps in the FPGA CAD flow. These tools are developed in direct extension of the popular Versatile Place and Route (VPR) academic tools. We describe the changes that are necessary to the traditional tools in order to model modern devices, and provide experimental results that show the quality of the algorithms achieved is similar to a commercial CAD tool, Quartus II. Finally, a small number of research experiments were investigated using the clustering and placement tools created to demonstrate the practical use of these tools for academic research studies of FPGA CAD tools.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.999

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 science0.0010.000
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 head0.028
GPT teacher head0.262
Teacher spread0.234 · 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 designOther design
Domainnot available
GenreEmpirical

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

Citations9
Published2008
Admission routes1
Has abstractyes

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