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Record W2070633734 · doi:10.1049/el:20020192

RNS-enabled digital signal processor design

2002· article· en· W2070633734 on OpenAlexfundno aff
Javier Ramı́rez, Antonio García, Sergio López-Buedo, A. Lloris

Bibliographic record

VenueElectronics Letters · 2002
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Residue Arithmetic
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Council
KeywordsComputer scienceReduced instruction set computingSIMDVery-large-scale integrationResidue number systemDigital signalDigital signal processorComputer architectureDigital signal processingInstruction setSignal processingEmbedded systemLogic synthesisComputer hardwareLogic gateParallel computing

Abstract

fetched live from OpenAlex

Residue number system (RNS) is explored for implementation of fast digital signal processors with the design of an RNS-based SIMD RISC processor. Simulations conducted on programmable logic show a sustained advantage over commercial chips for a representative set of applications, while prospective results on VLSI technology are also promising.

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: Methods · Consensus signal: Methods
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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.184
Teacher spread0.173 · 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
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

Citations66
Published2002
Admission routes1
Has abstractyes

Explore more

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