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Record W4231725977 · doi:10.32920/ryerson.14645058.v1

The effect of multi-bit correlation on the design of routing resources in field programmable gate arrays

2021· preprint· en· W4231725977 on OpenAlexaff
Ping Chen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsField-programmable gate arrayComputer scienceRouting (electronic design automation)Computer architecturePath (computing)Electronic circuitArchitectureEmbedded systemParallel computingComputer engineeringComputer hardwareComputer networkEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The large arithmetic-intensive applications increasingly implemented on field-programmable gate arrays (FPGAs) challenge FPGA architects to design FPGAs that can efficiently transport large amount of multi-bit wide signals in the data-path circuits of these applications. In this work, we investigate the area efficiency of two FPGA multi-bit aware routing architectures - the sparse and the enhanced sparse architectures, and compare them with the conventional and the configuration memory sharing architectures. We found that the sparse and enhanced sparse architectures are 6-10% more efficient than the conventional architecture. Our data also show that while the configuration memory sharing architecture can achieve the highest level of theoretical area savings for multi-bit transportation, it performs poorly for circuits with 50% or less multi-bit signals. These results suggest that FPGA architects should look beyond conventional architectures in order to create more efficient routing architectures for modern FPGAs.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.654

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.220
Teacher spread0.205 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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