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Record W2560205416 · doi:10.1109/mcsoc.2016.12

Adaptive VC Organization and Arbitration for Efficient NoC Design

2016· article· en· W2560205416 on OpenAlexafffund
Masoud Oveis-Gharan, Gul N. Khan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRouterComputer scienceNetwork on a chipCore routerArbitrationOne-armed routerComputer networkLatency (audio)Virtual channelNetwork packetThroughputQueueDistributed computingEmbedded systemQueueing theoryOperating systemWireless

Abstract

fetched live from OpenAlex

Network-on-Chip (NoC) has emerged as one of the main communication structure suitable for the interconnection of processing and other IP cores of a system-on-chip (SoC). An NoC typically utilizes virtual channels (VCs) to improve wormhole routing among the SoC cores by enabling multiple data packets to share a communication channel and to avoid deadlocks. Dynamically allocation multi queues based VC organization has higher buffer utilization but it also has some problems such as complexity, setup limitation, etc. Arbitration is also an important part of NoC routers. Past arbitration techniques have some problems that are related to lower speed, fairness, and router pipelining. We present a novel NoC router (RDQ-IRR-v2) architecture that incorporates some solutions to all of these problems. The router design utilizes adaptive VC buffering and index based arbitration techniques. The experiments and simulations indicate the efficiency of our proposed NoC router in terms of power consumption, chip area, frequency of router as well as latency and throughput related performance metrics in different NoC configurations and for various traffic patterns.

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 score0.975
Threshold uncertainty score0.111

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 head0.024
GPT teacher head0.216
Teacher spread0.192 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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