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Record W2060212507 · doi:10.1109/dft.2011.12

A Fault Tolerant Hierarchical Network on Chip Router Architecture

2011· article· en· W2060212507 on OpenAlexaff
M.H. Neishaburi, Željko Žilić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsRetransmissionNetwork on a chipComputer scienceVirtual channelRedundancy (engineering)Fault toleranceRouterLatency (audio)Computer networkNetwork packetEmbedded systemEnergy consumptionDistributed computingEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

Continuing advances in the processing technology, along with the significant decreases in the feature size of integrated circuits lead to increases in susceptibility to transient errors and permanent faults. Network on Chips (NoCs) have come to address the demands for high bandwidth communication among processing elements. The structural redundancy inherited in NoC-based design can be exploited to improve reliability and compensate for the effects of failures. In this paper, we propose an enhanced fault tolerant micro architecture with deadlock-free routing for Hierarchical NoCs. The proposed router supplies dynamic virtual channel allocation, and it employs a high-performance fault tolerant control flow, handling both transient and permanent faults in hierarchical networks without extra retransmission buffer requirements. Experimental results show a significant improvement in reliability as well as decreases in the average latency and energy 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.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.219
Teacher spread0.193 · 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

Citations13
Published2011
Admission routes1
Has abstractyes

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