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Record W1702497178 · doi:10.1109/icm.2000.916414

Data-driven dynamic logic versus NP-CMOS logic, a comparison

2002· article· en· W1702497178 on OpenAlexaff
Reza Rafati, A.Z. Charaki, S.M. Fakhraie, K.C. Smith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCMOSPass transistor logicDomino logicLogic gateLogic familyLogic synthesisAND-OR-InvertComputer scienceDynamic logic (digital electronics)Logic optimizationLogic levelDissipationPull-up resistorElectronic engineeringElectrical engineeringAlgorithmEngineeringTransistorElectronic circuitPhysicsDigital electronicsVoltage

Abstract

fetched live from OpenAlex

Data-driven dynamic logic (D/sup 3/L) is an appropriate candidate for replacing conventional dynamic logic in many cases. In a previous paper, we have shown how a D/sup 3/L-based design can be used in place of a conventional domino logic (Rafati et al., ISCAS vol.1, pp.752-5, 2000). The designed circuit has been shown to have up to 30% less power dissipation compared to its domino counterpart. This is in addition to the fact that no speed degradation was found. In this paper, after a brief introduction on NP-CMOS logic and D/sup 3/L, we show how to convert an NP-CMOS design to a D/sup 3/L one. Then, the results of simulations performed on a 16-bit barrel shifter are demonstrated and compared for static, NP-CMOS and D/sup 3/L design styles.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.148
GPT teacher head0.337
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations9
Published2002
Admission routes1
Has abstractyes

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