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Record W2075501204 · doi:10.1109/mwscas.2010.5548774

Efficient VLSI implementation of a finite field multiplier using reordered normal basis

2010· article· en· W2075501204 on OpenAlexafffund
Karl Leboeuf, Ashkan Hosseinzadeh Namin, Huapeng Wu, Roberto Muscedere, Majid Ahmadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCoding theory and cryptography
Canadian institutionsUniversity of Windsor
FundersCMC Microsystems
KeywordsNISTMultiplier (economics)Clock rateVery-large-scale integrationFinite fieldComputer scienceElliptic curve cryptographyNormal basisDomino logicCryptographyArithmeticParallel computingCMOSApplication-specific integrated circuitPublic-key cryptographyComputer hardwareEmbedded systemChipLogic synthesisElectronic engineeringLogic gateMathematicsAlgorithmEngineeringGalois theoryDiscrete mathematicsEncryptionLogic family

Abstract

fetched live from OpenAlex

A new VLSI implementation for a finite field multiplier using reordered normal basis is presented. The hardware architecture uses domino logic building blocks as well as True Single Phase Clock (TSPC) flip-flops to achieve exceptional performance. The multiplier has been realized in a 0.18 µm CMOS process and can perform multiplication correctly up to a clock rate of 1.789 GHz, requiring 62048 µm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of silicon area. Compared to similar implementations, the new design yields a 43% reduction in area utilization, and a 12% increase in maximum operating speed. The size of the multiplier, 233, is recommended by the National Institute of Standard and Technology (NIST) for elliptic key cryptography. Finite field multipliers such as the proposed one have applications in public key cryptography for constrained devices such as smart cards or hand held devices.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score0.379

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)0.0000.000

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 head0.013
GPT teacher head0.277
Teacher spread0.264 · 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 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

Citations4
Published2010
Admission routes2
Has abstractyes

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