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Record W2066007382 · doi:10.1145/2591513.2591581

Highly adaptive and congestion-aware routing for 3D NoCs

2014· article· en· W2066007382 on OpenAlexaff
Manoj Kumar, Vijay Laxmi, Manoj Singh Gaur, Masoud Daneshtalab, Seok‐Bum Ko, Mark Zwoliński

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Saskatchewan
FundersUK-India Education and Research Initiative
KeywordsComputer scienceDeadlockVirtual channelPolygon meshComputer networkDistributed computingNetwork packetChannel (broadcasting)Dependency (UML)Routing (electronic design automation)Airfield traffic patternNetwork congestionAlgorithm

Abstract

fetched live from OpenAlex

In this paper, we propose a novel highly adaptive and congestion aware routing algorithm 3D meshes which is equally applicable to 2D meshes as well. The proposed algorithm allows cyclic dependencies in channel dependency graph (CDG) providing higher degree of adaptiveness. The algorithm uses congestion-aware channel selection strategy that results balanced distribution of traffic flows across the network. A packet follows non-minimal paths only when minimal paths are congested at the neighboring channels. The deadlock avoidance methodology adopted by our algorithm remains cost-efficient as it uses one extra virtual channel along each of Y and Z dimensions to achieve deadlock freedom.

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 score0.984
Threshold uncertainty score0.208

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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