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Record W2121969545

Via-programmable logic array VPEX2 with configurable DFF using 2 logic elements

2009· article· en· W2121969545 on OpenAlexaff
Takeshi Fujino, Nishimoto Tomohiro, Yuichi Kokusyo, Masaya Yoshikawa, Guy Lemieux

Bibliographic record

VenueProceedings of the 2009 12th International Symposium on Integrated Circuits · 2009
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProgrammable logic arrayProgrammable logic deviceProgrammable Array LogicLogic gateLogic familyPass transistor logicSimple programmable logic deviceAND-OR-InvertComputer scienceLogic synthesisMacrocell arrayLogic optimizationSequential logicElectronic circuitComputer hardwareElectronic engineeringArithmeticDigital electronicsAlgorithmEngineeringMathematicsElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

We have been studied the VPEX (Via Programmable logic using EXclusive or array) architecture whose logic element (LE) consist of the combination of AOI21, NOR and NOT gates. We adopted “configurable DFF architecture” which means that a DFF can be configured using few LEs on demand. In the former VPEX architecture called VPEX1, 4 LEs are needed to configure a DFF. In the new VPEX architecture called VPEX2, a DFF can be composed of 2 LEs by applying novel technique in which NOR gate is changed to 2 transmission gates by the isolation of active region. The sample circuits area are 20–30% decreased because of the reduction of DFF area. Furthermore, auxiliary NOT gate is attached to the LE, and the circuit composition for 6 logic functions are optimized. The gate delay time for optimized logic functions are decreased by 20–50%. These data suggests that the VPEX2 LE will be a good candidate for practical via-programmable logic devices.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.556
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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 designBench or experimental
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
Published2009
Admission routes1
Has abstractyes

Explore more

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