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Record W4249748885 · doi:10.1145/503053.503056

A flexible floating-point format for optimizing data-paths and operators in FPGA based DSPs

2002· article· en· W4249748885 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
FieldComputer Science
TopicDigital Filter Design and Implementation
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceFloating pointRepresentation (politics)Point (geometry)Computer hardwareDigital signal processingNoise (video)ReducerData streamEmbedded systemAlgorithmArtificial intelligenceEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Video signal processing requires complex algorithms performing many basic operations on a video stream. To perform these calculations in real-time in a FPGA, we must use innovative structures to meet speed requirements while managing complexity. As part of a project aiming at the development of a video noise reducer, we developed an optimized processing stream that required some floating-point calculations. This paper presents the rationale for developing a floating-point unit, justifies the data representation used, its implementation in a Xilinx VirtexE FPGA and reports the performance we obtained. A divider using this representation is also presented, with its implementation and performances in the same FPGA.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.983
Threshold uncertainty score0.430

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 communication0.0000.003
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 head0.105
GPT teacher head0.298
Teacher spread0.193 · 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

Citations5
Published2002
Admission routes1
Has abstractyes

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