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Record W2117584697 · doi:10.1109/ccece.2011.6030434

Flexible simulation and modeling for 2D topology NoC system design

2011· article· en· W2117584697 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSystemCNetwork on a chipNetwork topologyComputer scienceRouting (electronic design automation)Key (lock)System on a chipEmbedded systemComputer architectureTopology (electrical circuits)EngineeringComputer network

Abstract

fetched live from OpenAlex

Network on Chip (NoC) is a new paradigm that can solve the problems related to System on Chip (SoC) design. These problems and challenges become more significant when the complexity of SoC increases. The vast volume of these challenges requires a new and flexible NoC test framework (simulator) to be investigated. We have developed a general purpose SystemC based NoC simulator that employs some new approaches to have a superior test system as compared to the past NoC simulators. The simulator mainly target 2D regular NoC topologies where deterministic and adaptive routing techniques that play a key role in the NoC performance. Various NoC design parameters are analyzed by trading off throughputs and latencies. The results show that for some cases the new Line-Probe routing is faster and contention free for local traffic in Torus topology.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.192

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.167
GPT teacher head0.300
Teacher spread0.133 · 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

Quick stats

Citations13
Published2011
Admission routes1
Has abstractyes

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