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Record W1991225420 · doi:10.1049/iet-cdt.2011.0173

Contention‐aware selection strategy for application‐specific network‐on‐chip

2013· article· en· W1991225420 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

VenueIET Computers & Digital Techniques · 2013
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceSelection (genetic algorithm)Routing (electronic design automation)Network on a chipStatic routingNetwork packetSelection algorithmAdaptive routingRouting protocolComputer network

Abstract

fetched live from OpenAlex

Network‐on‐chip (NoC) performance largely depends on the underlying deadlock‐free and efficient routing algorithm. The effectiveness of any adaptive routing algorithm strongly depends on the underlying selection strategy. When the routing function returns a set of admissible output channels with cardinality greater than one, a selection function is used to select the output channel to which the packet will be forwarded. In this study a novel selection strategy, LATEX, is proposed that can be used with any adaptive routing algorithm for specified applications. The objective of the proposed selection strategy is to efficiently balance traffic load and reach better performance results. Performance evaluation is carried out by using a flit‐accurate simulator under two real traffic scenarios. Result experiments show that the proposed selection strategy applied to several routing algorithms significantly improves average delay, max delay and power consumption.

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 categoriesScholarly communication
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 score0.974
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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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