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Record W2118995101 · doi:10.1109/ccece.1999.807245

High-speed image composition with enhanced multiplier structures

2003· article· en· W2118995101 on OpenAlexaff
Bo Liu, Martin Margala, N.G. Durdle, Scott Juskiw

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsTellabs (Canada)University of Alberta
Fundersnot available
KeywordsAdderComputer scienceMultiplier (economics)Carry-save adderSubtractionMatrix multiplicationArithmeticCarry (investment)Computer hardwareParallel computingMathematicsLatency (audio)Telecommunications

Abstract

fetched live from OpenAlex

Describes an efficient implementation of a fused multiplier-adder-subtracter circuit for image composition. An enhanced multiplier structure achieves high-speed composition with virtually no additional hardware or latency over that of a conventional multiplication cell. Expansion of the multiplier partial product matrix permits the parallel addition of multiple terms without the intrinsic delay incurred with a carry propagation. A modified encoding paradigm allows "on-the-fly" term negation to realize subtraction. An optimal design strategy for carry-save adder arrays performs carry-propagation addition in parallel with reduction of the partial product matrix. The proposed fused-cell architecture is general enough to support a variety of custom functions and exceeds the performance requirements of a full-frame real-time image generation system.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.007
GPT teacher head0.231
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations3
Published2003
Admission routes1
Has abstractyes

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