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Record W2173609013 · doi:10.1155/2008/736203

The Coarse-Grained/Fine-Grained Logic Interface in FPGAs with Embedded Floating-Point Arithmetic Units

2008· article· en· W2173609013 on OpenAlexaff
Chi Wai Yu, Julien Lamoureux, Steven J. E. Wilton, Philip H. W. Leong, Wayne Luk

Bibliographic record

VenueInternational Journal of Reconfigurable Computing · 2008
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of British Columbia
FundersEngineering and Physical Sciences Research Council
KeywordsField-programmable gate arrayComputer scienceInterface (matter)Parallel computingFloating pointRouting (electronic design automation)Point (geometry)Embedded systemAlgorithmMathematicsGeometry

Abstract

fetched live from OpenAlex

This paper examines the interface between fine-grained and coarse-grained programmable logic in FPGAs. Specifically, it presents an empirical study that covers the location, pin arrangement, and interconnect between embedded floating point units (FPUs) and the fine-grained logic fabric in FPGAs. It also studies this interface in FPGAs which contain both FPUs and embedded memories. The results show that (1) FPUs should have a square aspect ratio; (2) they should be positioned near the center of the FPGA; (3) their I/O pins should be arranged around all four sides of the FPU; (4) embedded memory should be located between the FPUs; and (5) connecting higher I/O density coarse-grained blocks increases the demand for routing resources. The hybrid FPGAs with embedded memory required 12% wider channels than the case where embedded memory is not used.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.243
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations8
Published2008
Admission routes1
Has abstractyes

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