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Record W2103117726 · doi:10.1109/tvlsi.2005.859561

Routing architecture optimizations for high-density embedded programmable IP cores

2005· article· en· W2103117726 on OpenAlexaff
Peter Hallschmid, Steven J. E. Wilton

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2005
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRouting (electronic design automation)Logic blockBlock (permutation group theory)Programmable logic arrayProgrammable logic deviceParallel computingChannel (broadcasting)Computer scienceProgrammable Array LogicGate arrayLogic gateSimple programmable logic deviceBlock sizeSquare (algebra)Field-programmable gate arrayTopology (electrical circuits)Logic synthesisComputer hardwareLogic familyEmbedded systemEngineeringAlgorithmMathematicsElectrical engineeringTelecommunicationsKey (lock)Geometry

Abstract

fetched live from OpenAlex

Programmable logic cores differ from stand-alone field-programmable gate arrays in that they can take on a variety of shapes and sizes. With this in mind, we investigate the detailed routing architecture of rectangular programmable logic cores. We quantify the effects of having different X and Y channel capacities and show that the optimum ratio between the X and Y channel widths for a rectangular core is between 1.2 and 1.5. We also present a new switch block family optimized for rectangular cores. Further, we quantify the effects of logic block pin placement. Compared with a simple extension of an existing switch block, our new architecture leads to a density improvement of up to 11.9%. Finally, we show that, if the channel width, switch block, and pin placement are chosen carefully, then the penalty for using a rectangular core (compared to a square core with the same logic capacity) is small; for a core with an aspect ratio of 2:1, the area penalty is 1.6% and the speed penalty is 3.8%.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score1.000

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 head0.010
GPT teacher head0.223
Teacher spread0.214 · 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.

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

Citations0
Published2005
Admission routes1
Has abstractyes

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