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Record W2150768199 · doi:10.1109/newcas.2010.5604017

Dual-edge triggered pulsed energy recovery flip-flops

2010· article· en· W2150768199 on OpenAlexaff
S. E. Esmaeili, A.J. Al-Khalili, Glenn Cowan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsConcordia University
Fundersnot available
KeywordsSignal edgeFlip-flopComputer scienceEnhanced Data Rates for GSM EvolutionFLOPSCMOSElectronic engineeringEnergy (signal processing)Power (physics)SIGNAL (programming language)Clock rateClock signalReduction (mathematics)TelecommunicationsEngineeringPhysicsComputer hardwareParallel computingDigital signal processingMathematicsJitter

Abstract

fetched live from OpenAlex

Resonant clocking is an emerging effective method for reducing power consumption in the clock distribution network. In this technique a resonant (sinusoidal) clock replaces the traditional square wave clock signal. In this paper we combine the emerging resonant clocking technique with the well known dual-edge triggering scheme to enable further power reduction in the clock tree. We propose dual-edge triggering in three pulsed flip-flops that operate with a sinusoidal clock signal; namely: the Static Differential Energy Recovery (SDER) flip-flop, the Differential Conditional Capturing Energy Recovery (DCCER) flip-flop, and the Single-ended Conditional Capturing Energy Recovery (SCCER) flip-flop. The proposed dual-edge flip-flops were tested using STMicroelectronics 90nm process technology. Simulation results show the correct operation of the dual-edge triggered flip-flop at a frequency of 250MHz with throughput of 500 MHz.

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), Insufficient payload (model declined to judge)
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 score0.906
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)0.0020.000

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 head0.007
GPT teacher head0.181
Teacher spread0.175 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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