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Record W3135842381 · doi:10.1049/cdt2.12026

Low‐space bit‐serial systolic array architecture for interleaved multiplication over GF(2 <sup> <i>m</i> </sup> )

2021· article· en· W3135842381 on OpenAlexaff
Atef Ibrahim

Bibliographic record

VenueIET Computers & Digital Techniques · 2021
Typearticle
Languageen
FieldComputer Science
TopicCoding theory and cryptography
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMultiplier (economics)Very-large-scale integrationArithmeticApplication-specific integrated circuitSystolic arrayComputer scienceMultiplication (music)GF(2)Binary numberCryptographyFinite fieldComputer hardwareParallel computingMathematicsAlgorithmEmbedded systemDiscrete mathematicsCombinatorics

Abstract

fetched live from OpenAlex

Abstract This article offers a new bit‐serial systolic array architecture to implement the interleaved multiplication algorithm in the binary‐extended field. The exhibited multiplier structure is more proper for VLSI implementation as it has regular cell structures as well as local communication wires between the cells. The ASIC implementation results of the suggested bit‐serial multiplier structure and the existing competitive bit‐serial multiplier structures previously described in the literature indicate that the recommended design achieves a notable reduction in area and significant improvement of area‐time complexities by at least 28.4% and 35.7%, respectively. Therefore, it is more proper for cryptographic applications forcing more restrictions on the space.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations7
Published2021
Admission routes1
Has abstractyes

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