Smashing the Implementation Records of AES S-box
Bibliographic record
Abstract
Canright S-box has been known as the most compact S-box design since its introduction back in CHES’05. Boyar-Peralta proposed logic-minimization heuristics that could reduce the gate count of Canright S-box from 120 gates to 113 gates, however synthesis results did not reflect much improvement. In CHES’15, Ueno et al. proposed an S-box that has a slightly higher area, but significantly faster than the previous designs, hence it was the most efficient (measured by area×delay) S-box implementation to date. In this paper, we propose two new designs for the AES S-box. One design has a smaller implementation area than both Canright and the 113-gate S-boxes. Hence, our first design is the smallest AES S-box to date, breaking the 13 years implementation record of Canright. The second design is faster and smaller than the Ueno S-box. Hence, our second design is both the fastest and the most efficient S-box design to date. While doing so, we also propose new logicminimization heuristics that outperform the previous algorithms of Boyar-Peralta. Finally, we conduct an exhaustive evaluation of each and every block in the S-box circuit, using both structural and behavioral HDL modeling, to reach the optimum synergy between theoretical algorithms and technology-supported optimization tools. We show that involving the technology-supported CAD tools in the analysis results in several counter-intuitive results.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".