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Record W2060161795 · doi:10.1109/cads.2013.6714252

A novel test strategy and fault-tolerant routing algorithm for NoC routers

2013· article· en· W2060161795 on OpenAlexaff
Sanaz Sadat Alamian, Ramin Fallahzadeh, Shaahin Hessabi, Javad Alirezaie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRouterComputer scienceNetwork on a chipLatency (audio)Network packetComputer networkOne-armed routerRouting algorithmNetwork routingCore routerDeadlockRouting (electronic design automation)MetricsRouting protocolRouting tableDistributed computingEmbedded system

Abstract

fetched live from OpenAlex

In this paper, we present a novel routing algorithm in order to avoid deadlock and packet dropping. In our proposed algorithm the network-on-chip (NoC) is capable of tolerating faults in presence of control faults in combinational parts of routers. In addition, by modifying the functionality of the router, the router is enabled to test its own, as well as the preceding router's functionality based on the routing algorithm, destination address and previous router's situation. Each router recognizes the faulty neighbor and announces it to successive routers. In this scheme no extra packets will be generated. We analyze the effects of our method on latency, power consumption and drop rate. Our experimental results illustrate that, fault coverage for routers can reach up to 100% with yet low power consumption and significant improvement in latency compared to the baseline approach.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.234
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 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

Citations8
Published2013
Admission routes1
Has abstractyes

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