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Record W1932810063 · doi:10.1109/ccece.2005.1557022

A 1.8V 1.1GHz novel digital multiplier

2006· article· en· W1932810063 on OpenAlexaff
A.A. Khatibzadeh, Kaamran Raahemifar, Majid Ahmadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsUniversity of WindsorToronto Metropolitan University
Fundersnot available
KeywordsMultiplier (economics)Power consumptionCMOSComputer scienceFrequency multiplierAnalog multiplierPairwise comparisonElectronic engineeringTopology (electrical circuits)Power (physics)Electrical engineeringComputer hardwareEngineeringDigital signal processingArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

This paper presents the design of an 8 times 8-bit novel digital multiplier providing a better performance than the conventional linear array multipliers in two folds of speed and power consumption. The modified pairwise and parallel addition algorithms provide high speed multiplication in this work. The power performance of individual block is pre-evaluated to identify the most power consuming element and attempt is to select the most efficient topology to reduce the power consumption of entire multiplier while maintaining the high operating frequency. The proposed multiplier has been designed and implemented employing TSMC 0.18 mum CMOS technology and analyzed using HSPICE. When the multiplier is targeted to a maximum operating frequency of 1.1 GHz at V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DD</sub> equal to 1.8 V, it dissipates 22 mW. For comparison purposes a Baugh-Wooley multiplier is redesigned and optimized. The simulation results are compared showing superiority of proposed multiplier in both power and speed performance

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.003
GPT teacher head0.147
Teacher spread0.144 · 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.

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
Published2006
Admission routes1
Has abstractyes

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