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Record W2121091932 · doi:10.1109/rsp.2009.37

Rapid Prototyping Projection Algorithms with FPGA Technology

2009· article· en· W2121091932 on OpenAlexafffund
John L. Cole, L.E. Garey, Kenneth B. Kent

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMatrix Theory and Algorithms
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsToeplitz matrixField-programmable gate arrayProjection (relational algebra)Computer scienceAlgorithmAccelerationField (mathematics)MathematicsEmbedded system

Abstract

fetched live from OpenAlex

Linear systems with Toeplitz coefficient matrices often appear in applied science problems. Systems of this form arise as a result of finite difference methods when applied to approximate differential Equations with boundary conditions. The sparse structure of Toeplitz matrices lend themselves well to iterative algorithms, such as projection methods, and are favored techniques for solving large systems. Field Programmable Gate Arrays (FPGAs) have been growing in popularity among the scientific community due to the potential for increased performance when evaluating mathematical operations. The regular, sparse, structure inherent in Toeplitz systems makes it suitable for FPGA acceleration. Here, a framework is developed to support the efficient development of projection algorithms in an FPGA. Results of applying the framework to two projection algorithms are presented.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.839
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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
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

Citations0
Published2009
Admission routes2
Has abstractyes

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