A High-Speed, Fully-Pipelined VLSI Architecture for Real-Time AES
Bibliographic record
Abstract
Nowadays, data encryption and decryption have become mandatory for any real-time communication applications. We propose a novel, area-speed efficient, high-speed architecture for the Advanced Encryption Standard hardware implementation. Our proposed architecture utilizes the composite field technique for SubBytes/InvSubBytes transformation instead of the traditionally-used look up table technique. As a result, the unbreakable delay of using look up tables in the traditional technique is eliminated. This, in turn, enables sub-pipelining implementation for further speeding up. Moreover, composite field arithmetic is employed to reduce the critical path delay. We propose a new algorithm to generate the optimum isomorphic mapping matrix, which reduces the critical path delay dramatically. In addition, an efficient key expansion architecture suitable for real-time applications is presented. Using the proposed architecture, a fully sub-pipelined implementation with 6 sub-stages in each round can achieve a throughput of 49.401 Gbps on a Xilinx XC2V6000FF1152-6 device in non-feedback mode, which is twice faster than the fastest Advanced Encryption Standard FPGA implementation known to date.
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 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 | 0.000 |
| 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) | 0.004 | 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".