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Record W2164405342 · doi:10.1109/ccece.1998.682733

Low-voltage power-efficient BiDPL logic design and applications

2002· article· en· W2164405342 on OpenAlexaff
Martin Margala, N.G. Durdle, N.L. Rodnunsky

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAdderPass transistor logicCMOSLogic gateElectrical engineeringLogic levelElectronic engineeringPower (physics)VoltageLogic familyComputer sciencePull-up resistorLow-power electronicsLogic synthesisTransistorBiCMOSEngineeringPhysicsPower consumption

Abstract

fetched live from OpenAlex

This paper presents a new logic design, bipolar double pass-transistor logic (BiDPL), and its implementation into a full-adder. At 1.2 V and output loads of 0.1 to 0.7 pF the new logic style has up to 2.9 times better power efficiency than previously reported low-voltage BiCMOS styles and uses between 16 to 32 % less switching power. Under optimal conditions (V/sub dd/=1.6 V), the new design has up to 18% higher power efficiency than conventional CMOS logic for loads of 0.55 to 1 pF and up to 117 % better power efficiency compared to BiCMOS styles for output loads of 0.1 to 0.68 pF. When used to implement a full adder, it is more power-efficient at very low power supply voltages (1.1 to 2 V) than a conventional CMOS adder design and the best low-voltage low-power adder reported in the literature. The proposed BiDPL adder outperforms in power efficiency both designs at 1.5 V by as much as 61 % and 535 % respectively.

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: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.192
Teacher spread0.178 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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