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Record W2105238901 · doi:10.1109/aspdac.2012.6165004

Using link-level latency analysis for path selection for real-time communication on NoCs

2012· article· en· W2105238901 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceMultiprocessingLatency (audio)Network on a chipComputer networkRouting (electronic design automation)InterconnectionDistributed computingParallel computing

Abstract

fetched live from OpenAlex

We present a path selection algorithm that is used when deploying hard real-time traffic flows onto a chip-multiprocessor system. This chip-multiprocessor system uses a priority-based real-time network-on-chip interconnect between the multiple processors. The problem we address is the following: given a mapping of the tasks onto a chip-multiprocessor system, we need to determine the paths that the traffic flows take such that the flows meet there deadlines. Furthermore, we must ensure that the deadline is met even in the presence of direct and indirect interference from other flows sharing network links on the path. To achieve this, our algorithm utilizes a link-level analysis to determine the impact of a link being used by a flow, and its affect on other flows sharing the link. Our experimental results show that we can improve schedulability by about 8% and 15% over Minimum Interference Routing and Widest Shortest Path algorithms, respectively.

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)0.001
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 score0.924
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.112
GPT teacher head0.322
Teacher spread0.211 · 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

Quick stats

Citations9
Published2012
Admission routes1
Has abstractyes

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