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Record W2769458067

Worst Case Latency Analysis for Hoplite FPGA-based NoC

2017· article· en· W2769458067 on OpenAlexfundno aff
Saud Wasly, Rodolfo Pellizzoni, Nachiket Kapre

Bibliographic record

VenueUWSpace (University of Waterloo) · 2017
Typearticle
Languageen
FieldComputer Science
TopicVLSI and Analog Circuit Testing
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsField-programmable gate arrayComputer scienceLatency (audio)Embedded systemCase analysisParallel computingArtificial intelligenceTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Overlay NoCs, such as Hoplite, are cheap to implement on an FPGA but provide no bounds on worst-case routing latency of packets traversing the NoC due to deflection routing. In this paper, we show how to adapt Hoplite to enable calculation of precise upper bounds on routing latency by modifying the routing function to prioritize deflections, and by regulating the injection of packets to meet certain throughput and burstiness constraints. We provide an
\nanalytical model for computing end-to-end latency in the form of (1) in-flight time in the network $T^f$, and (2) waiting time at the source node $T^s$. To bound in-flight time in an $m \\times m$ NoC, we modify the routing function
\nand switching crossbar richness in the Hoplite router to deliver $T^{f} =\\Delta X + \\Delta Y + (\\Delta Y \\times m) + 2$ where $\\Delta X$ and $\\Delta Y$ are differences of the source and destination address co-ordinates of the
\npacket. To bound the waiting time at the source, we add a Token Bucket regulator with rate $\\rho_i$ and burstiness $\\sigma_i$ for each flow $f_i$node $(x,y)$ to deliver $(\\lceil\\frac{1}{\\rho_{_i}}\\rceil -1 ) + T^s$ : $T^s =\\lceil\\frac{\\sigma(\\Gamma^C_f){1-\\rho(\\Gamma^C_f)} \\rceil$ which depends on the regulator period $1/\\rho_i$, burstiness $\\sigma$ and the rate $\\rho$ of all interfering flows $\\Gamma^C_f$. A 64b implementation of our HopliteRT routerrequires $\\approx$4\\% fewer LUTs, and similar number of FFs compared to the original Hoplite router. We also need two small counters at each client port for regulating injection. We evaluate our model and RTL implementation across synthetic traffic patterns and observe behavior that conforms with the analytical bounds.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.997

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.026
GPT teacher head0.220
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 teacher head, 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

Citations3
Published2017
Admission routes1
Has abstractyes

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