MétaCan
Menu
Back to cohort
Record W2289940563 · doi:10.1002/sec.1462

Binary field multiplication on ARMv8

2016· article· en· W2289940563 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.

Bibliographic record

VenueSecurity and Communication Networks · 2016
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Residue Arithmetic
Canadian institutionsUniversity of Waterloo
FundersDivision of Electrical, Communications and Cyber SystemsInstitute for Information and Communications Technology Promotion
KeywordsMultiplication (music)Computer scienceBinary numberArithmeticField (mathematics)PolynomialParallel computingMathematicsCombinatoricsPure mathematics

Abstract

fetched live from OpenAlex

Abstract In this paper, we show efficient implementations of binary field multiplication over ARMv8. We exploit an advanced 64‐bit polynomial multiplication ( PMULL ) supported by ARMv8 and conduct multiple levels of asymptotically faster Karatsuba multiplication for polynomial multiplication. Finally, our method completed binary field multiplication within 57 and 153 clock cycles for B‐251 and B‐571 cases, respectively. Proposed method improves the speed‐performance by a factor of 4.5 times than previous techniques on same target platform. Copyright © 2016 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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.227

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 science0.0010.000
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 head0.008
GPT teacher head0.225
Teacher spread0.218 · 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