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Record W3130406170 · doi:10.1109/tvlsi.2021.3058509

An Efficient and High-Speed Overlap-Free Karatsuba-Based Finite-Field Multiplier for FGPA Implementation

2021· article· en· W3130406170 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Very Large Scale Integration (VLSI) Systems · 2021
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Residue Arithmetic
Canadian institutionsUniversity of Windsor
FundersFedDev Ontario
KeywordsOperandElliptic curve cryptographyField-programmable gate arrayCryptographyComputer scienceMultiplier (economics)Finite fieldPublic-key cryptographyKey (lock)Gate arrayMultiplication (music)Computer hardwareParallel computingArithmeticEmbedded systemAlgorithmMathematicsEncryption

Abstract

fetched live from OpenAlex

Cryptography systems have become inseparable parts of almost every communication device. Among cryptography algorithms, public-key cryptography, and in particular elliptic curve cryptography (ECC), has become the most dominant protocol at this time. In ECC systems, polynomial multiplication is considered to be the most slow and area consuming operation. This article proposes a novel hardware architecture for efficient field-programmable gate array (FPGA) implementation of Finite-field multipliers for ECC. Proposed hardware was implemented on different FPGA devices for various operand sizes, and performance parameters were determined. Comparing to state-of-the-art works, the proposed method resulted in a lower combinational delay and area-delay product indicating the efficiency of design.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.916
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.266
Teacher spread0.254 · 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