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Record W1959899082 · doi:10.1109/its.1990.175654

A neural network controller for a high-speed packet switch

2002· article· en· W1959899082 on OpenAlexaff
M. Mehmet Ali, H.T. Nguyen

Bibliographic record

VenueSBT/IEEE International Symposium on Telecommunications · 2002
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceArtificial neural networkIntegrated Services Digital NetworkNetwork packetThroughputAsynchronous Transfer ModeController (irrigation)Packet switchingQueueComputer networkAlgorithmReal-time computingTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

A neural network implementation of an input access scheme in a high-speed packet switch for broadband ISDN (integrated services digital network) is presented. In this switch, each input maintains a separate queue for each of the outputs, thus n/sup 2/ input queues in a (n*n) switch. Using synchronous operation, at most one packet per input and output will be transferred in any slot. A neural network maximizing the throughput of this switch is determined, and the energy function, its optimized parameters, and the connection matrix are given. Comparison of simulations with analytically derived upper and lower bounds shows close to optimal throughput. It is shown that this neural network may be implemented with existing technology for medium switch sizes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.000
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 head0.036
GPT teacher head0.273
Teacher spread0.237 · 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

Citations4
Published2002
Admission routes1
Has abstractyes

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