Investigation of compact hardware implementation of the advanced encryption standard
Bibliographic record
Abstract
A compact and efficient implementation of the advanced encryption standard (AES) is the desirable encryption IP core for any practical low-end embedded application. In this paper, we investigate various architectures for compact AES implementations in 0.18-/spl mu/m CMOS technology. We first investigate a new compact digital hardware implementation of AES s-boxes applying the discovery of linear redundancy in AES s-boxes. Although the new circuit has a small size, the speed of this implementation is also reduced. Encryption architectures without key scheduling employing four s-boxes and only one s-box are implemented using our new AES s-boxes, as well as based on other compact s-box structures. The comparison of six implementations indicates that the implementation using four s-boxes based on arithmetic operations in GF(2/sup 4/) has the best trade-off of area and speed. Therefore, using this s-box implementation, a complete encryption-decryption architecture with key scheduling employing the four s-box structure is implemented. In order to be adaptive to various practical applications, we optimize the implementation with the four s-box structure to support five different operation modes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".