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Record W2141133316 · doi:10.1109/ccece.2005.1557214

Single-chip FPGA implementation of a pipelined, memory-based AES Rijndael encryption design

2006· article· en· W2141133316 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsConcordia University
Fundersnot available
KeywordsAdvanced Encryption StandardComputer scienceEncryptionAES implementationsField-programmable gate arrayDatapathEmbedded systemByteLookup tablePipeline (software)CryptographyComputer hardwareParallel computingAlgorithmOperating system

Abstract

fetched live from OpenAlex

In this paper, we present a fully synchronous, memory-based, single-chip FPGA implementation of the recent AES standard, Rijndael encryption algorithm. Our RTL design encrypts the necessary AES rounds in an arithmetic pipeline structure. The dual-width encryption datapath uses lookup table (LUT) architecture to perform encryption with internally generated round keys. Rijndael state matrix cell entries are transformed individually at the byte-level for encryption operations such as cipher key addition, byte substitution, and shift row. Whereas, a 32-bit DSP core, inserted in the pipeline, allows for Galios field(8) arithmetic operations at the word-level of the state matrix column. Design functionality was verified using self-checking testbench with the NIST Known Answer Tests. Our FPGA implementation targets a Xilinx VirtexIIPro device. Experimental clock frequencies, throughput translations, latency-area issues and FPGA resource utilizations are presented for the memory-based design. Finally, we present a brief comparison of our FPGA implementation with other implementations of the Rijndael encryption algorithm

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.686
Threshold uncertainty score0.520

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 studies0.0000.000
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 head0.032
GPT teacher head0.282
Teacher spread0.251 · 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