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Record W4403561333 · doi:10.1016/j.vlsi.2024.102300

VLFF — A very low-power flip-flop with only two clock transistors

2024· article· en· W4403561333 on OpenAlexaff
Yugal Kishore Maheshwari, Manoj Sachdev

Bibliographic record

VenueIntegration · 2024
Typearticle
Languageen
FieldEngineering
TopicAnalog and Mixed-Signal Circuit Design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFlip-flopTransistorPower (physics)FlipElectrical engineeringComputer scienceEngineeringPhysicsCMOSVoltage

Abstract

fetched live from OpenAlex

Flip-flops (FFs) are an essential component of digital circuits, yet they use a lot of power and energy. This paper introduces the VLFF, an extremely low-power flip-flop that operates with just two single-phase clock transistors. The extracted simulation results show that VLFF is the most power-efficient FF amongst all examined FFs for the data activity (DA) range of 0% to 45%. Test-chip measurement results for the test-chip designed in TSMC CMOS 65 nm gp PDK demonstrate that at VDD = 1 V, power consumption is reduced by 63% and 16% with 12.5% DA, and 52% and 6% with 25% DA in comparison to TGFF and 18TSPC, respectively. • This paper proposed a very low-power flip-flop with only two single-phase clock transistors. • Proposed flip-flop is the most power-efficient amongst all considered. state-of-the-art flip-flops for data activities in the range of 0 – 45%. • Test-chip implemented in TSMC 65nm GP PDK validates the power savings of the proposed flip-flop over 18TSPC, and TGFF.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 communication0.0000.001
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 head0.005
GPT teacher head0.198
Teacher spread0.192 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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