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Record W2103884101 · doi:10.1049/iet-cdt.2010.0005

Dual-edge triggered sense amplifier flip-flop for resonant clock distribution networks

2010· article· en· W2103884101 on OpenAlexaff
S. E. Esmaeili, A.J. Al-Khalili, Glenn Cowan

Bibliographic record

VenueIET Computers & Digital Techniques · 2010
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsConcordia University
Fundersnot available
KeywordsFlip-flopSignal edgeElectronic engineeringClock rateClock signalReduction (mathematics)Power (physics)AmplifierSense amplifierEnhanced Data Rates for GSM EvolutionClock networkComputer scienceElectrical engineeringEngineeringSynchronous circuitVoltageElectronic circuitCMOSPhysicsTelecommunicationsDigital signal processing

Abstract

fetched live from OpenAlex

A dual-edge sense amplifier flip-flop (DE-SAFF) for resonant clock distribution networks (CDNs) is proposed. The clocking scheme used to enable dual-edge triggering in the proposed SAFF reduces short circuit power by allowing the precharging transistors to be switched on only for a portion of the clock period. The extracted circuit layout of the proposed DE-SAFF has been simulated in STMicroelectronics 90 nm technology with a resonant clock signal at a frequency of 500 MHz. Simulation results show correct functionality of the flip-flip under process, voltage and temperature variations. Two low-power clocking techniques, the dual-edge triggering method and the emerging resonant (sinusoidal) clocking technique, have been combined to enable further power reduction in the CDN. Modelling the resonant clock distribution system with the proposed flip-flop illustrates that dual-edge triggering can achieve up to 58% reduction in the power consumption of resonant clock networks.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0010.000
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 head0.006
GPT teacher head0.204
Teacher spread0.199 · 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 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

Citations18
Published2010
Admission routes1
Has abstractyes

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