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Record W2102653533 · doi:10.1109/4.910495

Dynamic current mode logic (DyCML): a new low-power high-performance logic style

2001· article· en· W2102653533 on OpenAlexaff
M.W. Allam, M.I. Elmasry

Bibliographic record

VenueIEEE Journal of Solid-State Circuits · 2001
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCurrent-mode logicPass transistor logicLogic familyLogic levelPull-up resistorLogic gateAdderElectronic engineeringEmitter-coupled logicDynamic logic (digital electronics)Electronic circuitComputer scienceResistor–transistor logicCMOSAdiabatic circuitIntegrated injection logicElectrical engineeringLogic synthesisVoltageEngineeringDigital electronicsTransistor

Abstract

fetched live from OpenAlex

This paper introduces a new reduced swing logic style called dynamic current mode logic (DyCML) that reduces both gate and interconnect power dissipation. DyCML circuits combine the advantages of MOS current mode logic (MCML) circuits with those of dynamic logic families to achieve high performance at a low-supply voltage with low-power dissipation. Unlike CML circuits, DyCML gates do not have a static current source, which makes DyCML a good candidate for portable devices and battery-powered systems. Simulation and test results show that DyCML circuits are superior to other logic styles in terms of power and delay. A 16-bit DyCML carry look-ahead adder (CLA), fabricated in 0.6-/spl mu/m CMOS technology, attains a delay of 1.24 ns and dissipates 19.2 mW at 400 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 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.257
Teacher spread0.242 · 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

Citations161
Published2001
Admission routes1
Has abstractyes

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