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Record W2130529591 · doi:10.1109/cicc.2002.1012766

Nearest neighbour interconnect architecture in deep submicron FPGAs

2003· article· en· W2130529591 on OpenAlex
A. Roopchansingh, Jonathan Rose

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsField-programmable gate arrayInterconnectionComputer scienceArchitectureNetwork topologyComputer architectureNearest neighbourParallel computingPerformance improvementEmbedded systemTopology (electrical circuits)EngineeringElectrical engineeringComputer networkArtificial intelligence

Abstract

fetched live from OpenAlex

Several commercial FPGA architectures provide fast connections between adjacent logic blocks that decrease the best-case delay between circuit elements with the goal of increasing overall performance. This paper explores the architecture of these Nearest Neighbour (NN) interconnects to determine topologies, quantities and distances that are best for performance and area. We show that certain architectures can achieve a 7.4% performance improvement at the cost of a 6.3% increase in total FPGA area when fully populated. We also show that a 6.4% improvement can be achieved for a more modest cost of 3.8% increase in area.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.501

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 head0.005
GPT teacher head0.183
Teacher spread0.178 · 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

Quick stats

Citations16
Published2003
Admission routes1
Has abstractyes

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