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Record W3203464045 · doi:10.1145/3479876.3481593

Worst-case latency analysis for the versal NoC network packet switch

2021· article· en· W3203464045 on OpenAlexaff
Ian Elmor Lang, Nachiket Kapre, Rodolfo Pellizzoni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceNetwork packetLatency (audio)Field-programmable gate arrayEmbedded systemNetwork on a chipScheduling (production processes)Automotive industryComputer networkEngineering

Abstract

fetched live from OpenAlex

The recent line of Versal FPGA devices from Xilinx Inc. includes a hard Network-On-Chip (NoC) embedded in the programmable logic, designed to be a high-performance system-level interconnect. While the target markets for Versal devices include applications with real-time constraints, such as automotive driver assist, the associated development tools only provide figures for "structural latencies" of data packets, which assume that the network is otherwise idle. In a realistic setting, this information is not enough to ensure deadlines are met, as different packets can contend for NoC switch outputs, which causes packet contents to be buffered while in transit, increasing their latency. In this work, we present a formal description of the NPS switches that compose the Versal NoC from a flit (or packet) scheduling perspective, based on the available cycle-accurate switch simulation code. We then analyze a scenario where network clients transfer data periodically over a single switch, and propose a method for calculating worst-case communication times in this scenario.

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)0.001
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 score0.982
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.027
GPT teacher head0.252
Teacher spread0.226 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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