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Record W2125319116 · doi:10.1142/s0218126607003472

SPTPL: A NEW PULSED LATCH TYPE FLIP-FLOP IN HIGH-PERFORMANCE SYSTEM-ON-A-CHIP (SoC)

2007· article· en· W2125319116 on OpenAlexfundno aff
Inhwa Jung, Moo-Young Kim, Chulwoo Kim

Bibliographic record

VenueJournal of Circuits Systems and Computers · 2007
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsnot available
FundersMinistry of Education and Human Resources DevelopmentCanadian Pulmonary Fibrosis Foundation
KeywordsFLOPSPipeline (software)Flip-flopPower (physics)Computer scienceClock networkChipElectronic engineeringFlip chipClock signalEmbedded systemElectronic circuitEngineeringElectrical engineeringCMOSClock skewParallel computingMaterials sciencePhysics

Abstract

fetched live from OpenAlex

In many VLSI chips, the power dissipation of the clocking system that includes clock distribution network and flip-flops is often the largest portion of total chip power consumption. In the near future, this portion is likely to dominate total chip power consumption due to higher clock frequency and deeper pipeline design trend. Traditionally, two approaches have been used: (1) to reduce power consumption in the clock tree, several low-swing clock flip-flops and double-edge flip-flops have been introduced; (2) to reduce power consumption in flip-flops, conditional capture, clock-on-demand, data-transition look-ahead techniques have been developed. Recently, pulsed latch type flip-flops are introduced in several high-performance microprocessors to reduce E × D. In this paper, these flip-flops are described with their pros and cons. Then, a new circuit technique is described along with simulation results. The proposed pulsed latch reduces E × D by 82.6% to 95.4% compared to conventional flip-flops.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.198
Teacher spread0.187 · 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 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

Citations1
Published2007
Admission routes1
Has abstractyes

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