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Record W2152035224 · doi:10.1109/ccece.1998.682732

200 MHz 0.8 μm CMOS gradient vector processor for real-time execution

2002· article· en· W2152035224 on OpenAlexaff
Martin Margala, N.G. Durdle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFrame rateComputer scienceFrame (networking)Vector processorPixelDivision (mathematics)Computer hardwareMedia processorSquare rootCMOSElectronic engineeringDigital signal processorArtificial intelligenceArithmeticEngineeringDigital signal processingMathematicsTelecommunications

Abstract

fetched live from OpenAlex

This paper describes a gradient vector processor which forms an important part of a shading processor being developed for a high resolution high performance real-time general purpose volume imaging system. The proposed architecture overcomes current image resolution and frame-rate limitations through the use of custom high-speed processors. The gradient vector processor evaluates three arithmetic operations: a square and add operation, square-root, and three division operations. Fully pipelined operation produces a vector every 5 ns or with a 200 MHz frequency at V/sub dd/=3 V and with an accuracy of /spl plusmn/0.78%. The algorithms and implementation in silicon are described in detail.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.590

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)0.0010.000

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 head0.198
Teacher spread0.187 · 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 designNot applicable
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

Citations1
Published2002
Admission routes1
Has abstractyes

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