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Record W2164724325 · doi:10.1109/newcas.2010.5603920

Using a pipelined S-box in compact AES hardware implementations

2010· article· en· W2164724325 on OpenAlexaff
Cheng Wang, Howard M. Heys

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAES implementationsComputer scienceEncryptionAdvanced Encryption StandardPipeline (software)Application-specific integrated circuitThroughputEmbedded systemImplementationComputer hardwareEncryption softwareKey (lock)Parallel computingDisk encryption40-bit encryptionOperating system

Abstract

fetched live from OpenAlex

Pipelined S-boxes are usually used in high speed hardware implementations of the Advanced Encryption Standard (AES), and not typically found in compact implementations because of the extra complexity added by the pipeline registers. In this paper, the area and speed performance of applying a pipelined S-box to compact AES hardware implementations is examined. A new compact AES encryption hardware core with 128-bit keys is proposed. The proposed design employs a single 4-stage pipelined S-box that is shared by t he data path operation and the key expansion operation. Compared with the previous smallest encryption-only ASIC implementation of AES, it achieves an increase in throughput of 2.1 times while maintaining a similar gate count. This result indicates that it is reasonable to consider using pipelined S-boxes in AES hardware implementations targeted at applications requiring low area and moderate speed.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.057
GPT teacher head0.375
Teacher spread0.318 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

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