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Record W2336651274 · doi:10.1109/jphot.2016.2526607

Bidirectional Transmission in an Optical Network on Chip With Bus and Ring Topologies

2016· article· en· W2336651274 on OpenAlexaff
S. Faralli, Fabrizio Gambini, Paolo Pintus, Mirco Scaffardi, Odile Liboiron-Ladouceur, Yule Xiong, P. Castoldi, Fabrizio Di Pasquale, Nicola Andriolli, Isabella Cerutti

Bibliographic record

VenueIEEE photonics journal · 2016
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcGill University
Fundersnot available
KeywordsNetwork topologyCrosstalkRing networkNetwork on a chipComputer scienceRing (chemistry)Silicon photonicsTopology (electrical circuits)PhotonicsBit error rateOptical switchChipElectronic engineeringComputer networkOptoelectronicsPhysicsTelecommunicationsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

In photonic integrated networks on chip (NoCs), microrings are commonly used for adding or dropping a single optical signal to be switched in the NoC. This paper demonstrates the feasibility of adding or dropping two optical signals at the same wavelength in the same microring of NoCs with bus and ring topology. More specifically, the same microring can be used to support simultaneous bidirectional transmissions of two signals to be coupled in the NoC topology, leading to two different configurations, called shared source-microring and shared destination-microring. Spectral characterization shows good agreement between simulations and measurements taken on silicon-based integrated NoC. Bit-error-rate (BER) measurements indicate that the shared sourcemicroring configuration performs better, achieving a penalty as low as 1.5 dB for a BER of 10-9at 10 Gb/s in the bus NoC. A higher penalty in the ring NoC for both configurations is due to higher crosstalk in the interconnecting ring.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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 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

Citations17
Published2016
Admission routes1
Has abstractyes

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