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Record W2537596455 · doi:10.1109/icm.2011.6177419

Transistor level optimization of sub-pipelined AES design in CMOS 65nm

2011· article· en· W2537596455 on OpenAlexaff
Abdallah Alma’aitah, Zine-Eddine Abid

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsQueen's University
Fundersnot available
KeywordsCMOSTransistorAdvanced Encryption StandardThroughputComputer scienceReduction (mathematics)DissipationEncryptionElectronic engineeringLogic gateLow-power electronicsPower consumptionEmbedded systemPower (physics)Electrical engineeringEngineeringVoltageAlgorithmMathematics

Abstract

fetched live from OpenAlex

Stage optimization of the hardware implementation of the popular encryption algorithms, the Advanced Encryption Standard (AES), is presented. The optimization, for lower power dissipation, is based on implementing the Multi Threshold CMOS (MTCMOS) technique in each of the AES stages. The critical paths are implemented using high performance gates based on high driving current transistors. For the optimized design, the Simulation results show about 10% reduction in power consumption compared to non optimized designs while maintaining the same throughput of 18Gbit/sec.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.127
GPT teacher head0.268
Teacher spread0.141 · 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 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

Citations1
Published2011
Admission routes1
Has abstractyes

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