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Record W2143584115 · doi:10.1109/iwsoc.2003.1213057

A new class of computational RAM architectures for real-time MPEG-4 applications

2004· article· en· W2143584115 on OpenAlexaff
Mohammed S. Sayed, Wael Badawy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceStatic random-access memoryEncoderSIMDParallel computingComputer architectureMemory architectureComputer hardwareMPEG-4Data compressionCoding (social sciences)Block (permutation group theory)Embedded systemProcess (computing)Algorithm

Abstract

fetched live from OpenAlex

This paper presents a new class of Computational RAM (C-RAM) architectures for real-time MPEG-4 applications. The proposed C-RAM architecture consists of an embedded SRAM and number of processing elements working in parallel to process the data stored in the memory. The processing elements are working as a single instruction multiple data (SIMD) architecture. Each processing element is used to process one memory column. The proposed class of C-RAM architectures has been used for MPEG-4 block-based motion estimation, which is the most computational intensive task in the encoder. The proposed architecture has been designed, prototyped, and simulated for 0.18 /spl mu/m CMOS TSMC technology. The simulation results show a promising performance of the proposed class of C-RAM architectures in video coding applications; it can process up to 126 frames per second with clock frequency 100 MHz.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.268
Teacher spread0.253 · 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 designBench or experimental
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
Published2004
Admission routes1
Has abstractyes

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