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Record W2895070398 · doi:10.1145/3241793.3241813

Flexible Reconfigurable On-chip Networks for Multi-core SoCs

2018· article· en· W2895070398 on OpenAlexafffund
Masoud Oveis-Gharan, Gul N. Khan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInterconnectionScalabilityEmbedded systemSystem on a chipChipNetwork on a chipKey (lock)Computer scienceComputer architectureComputer networkTelecommunications

Abstract

fetched live from OpenAlex

Multi and many-core embedded SoCs (System-on-Chip) provide key solutions to meet the extraordinary demands of current and future applications. This fact becomes critical when the chip design dives to the limitation of sub-nanometer technologies that cannot be shrunk further. Network on Chip (NoC) is a scalable interconnection structure, which can provide efficient solutions for on-chip interconnection problems of SoCs such as re-configurability required for multiple high-performance applications. Recent reconfigurable NoCs improve performance of SoC in exchange of larger chip area and higher power. In this paper, we present a novel high-performance reconfigurable NoC architecture that can improve the overall system performance for various SoC applications. Experimental results provide a comparison with the conventional on-chip interconnection structures to support our claim.

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 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 score0.910
Threshold uncertainty score0.440

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 head0.095
GPT teacher head0.310
Teacher spread0.215 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2018
Admission routes2
Has abstractyes

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