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Record W1482238126 · doi:10.1109/fccm.2001.41

System on a FPGA Virtual Concatenation

2001· article· en· W1482238126 on OpenAlexaff
Siren Sezer, Eileen Stewart, M. Carson, C. Greenwood

Bibliographic record

VenueField-Programmable Custom Computing Machines · 2001
Typearticle
Languageen
FieldComputer Science
TopicEmbedded Systems Design Techniques
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceConcatenation (mathematics)Nios IIComputer architectureEmbedded systemArchitectureAdaptation (eye)Transmission (telecommunications)Computer hardwareTelecommunications

Abstract

fetched live from OpenAlex

This paper presents the study and implementation of a novel architecture for a virtual concatenation circuit using the NIOS soft core embedded processor on a FPGA (APEX). The architecture is optimised for rapid adaptation of the virtual concatenation core by exploiting the reconfigurable properties of the FPGA technology and the programmable properties of embedded processors. This synergy provides a hardware efficient implementation of hitless re-configuration of transmission paths and the rapid adaptation of the architecture for a wide range of data transmission products to keep pace with product and standard migrations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.017
GPT teacher head0.269
Teacher spread0.252 · 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.

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

Citations0
Published2001
Admission routes1
Has abstractyes

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