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Record W2167843626 · doi:10.1109/ccece.2004.1345297

Design and implementation of enhanced crossbar CIOQ switch architecture

2004· article· en· W2167843626 on OpenAlexaff
Ammar Ahmad Awan, R. Venkatesan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCrossbar switchSpeedupComputer scienceVery-large-scale integrationScheduling (production processes)Queueing theoryThroughputArchitectureParallel computingStandard cellEmbedded systemComputer architectureIntegrated circuitComputer networkWirelessEngineeringOperating systemTelecommunications

Abstract

fetched live from OpenAlex

Currently, combined input output queued (CIOQ) switches are being considered for high performance switch architectures. This is due to their ability to achieve high throughput and emulate output queued switch performance with a small speedup factor of 4 or 5. The paper presents the design and VLSI implementation of a 16/spl times/16 cell based CIOQ switch with enhanced crossbars to provide a speedup of 4 while operating the switch fabric at the line rate and memories at half the line rate. We describe the implementation of this architecture in VLSI using 0.18 micron CMOS standard-cell technology. We report on the design complexity and discuss implementation results. This architecture can handle a line rate of 622 Mbps. The distributed cell scheduling and cell queueing and dequeueing allows this architecture to be scaled to a large number of inputs and outputs.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · 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 communication0.0010.000
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 head0.012
GPT teacher head0.267
Teacher spread0.255 · 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 designNot applicable
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

Citations7
Published2004
Admission routes1
Has abstractyes

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