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Record W1555508277 · doi:10.1002/cta.2020

A framework for high‐speed parallel‐prefix adder performance evaluation and comparison

2014· article· en· W1555508277 on OpenAlexafffund
Richard F. Hobson

Bibliographic record

VenueInternational Journal of Circuit Theory and Applications · 2014
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsAdderComputer scienceParallel computingDomino logicDominoCMOSPrefixArithmeticLogic gateEfficient energy useLogic synthesisTransistorElectronic engineeringLogic familyAlgorithmMathematicsElectrical engineeringVoltageEngineering

Abstract

fetched live from OpenAlex

Summary A new framework is proposed for the evaluation and comparison of high‐speed parallel‐prefix adders. The framework specifies input registers and latches and requires sum feedback for single cycle pipelined operation. Test pattern generation is also specified. A newly revised energy‐efficient 64‐bit carry select adder with distributed mixed valence logic to help reduce fan‐out and wire load is presented. Footless pulsed‐precharge domino and compound domino circuits, and smaller transistors help to reduce area and power. Detailed simulations with 65 nm CMOS models are compared with other parallel‐prefix adders that have been instantiated for comparison. Within this framework, energy reductions of 40% are obtained for the new adder versus two leading Kogge‐Stone designs, and 25% versus a new constant delay logic Sklansky style design, at similar cycle times. Copyright © 2014 John Wiley & Sons, Ltd.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.736
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.292
Teacher spread0.271 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2014
Admission routes2
Has abstractyes

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