A reconfigurable and compact subpipelined architecture for AES encryption and decryption
Bibliographic record
Abstract
Abstract AES has been used in many applications to provide the data confidentiality. A new 32-bit reconfigurable and compact architecture for AES encryption and decryption is presented and implemented in non-BRAM FPG in this paper. It can be reconfigured for the options of different key sizes which is very flexible for the users to apply AES for various application environments. The proposed design employs a single-round architecture and subpipeling to minimize the hardware cost. The fully composite field GF((2 4 ) 2 )-based encryption/decryption and keyschedule lead to the lower hardware complexity and efficient subpipelining for 32-bit data path. In addition, a new subpipelined on-the-fly keyschedule over composite field GF((2 4 ) 2 ) is proposed for all standard key sizes (128-, 192-, 256-bit) which generates the roundkeys simultaneously and efficiently. This feature is very useful and efficient when the main key has been changed since AES is a symmetric-key cryptography and the session key usually changes frequently. The proposed reconfigurable and compact design has higher throughput and lower hardware cost. It achieves throughputs of 375Mbits/s with 128-bit key, 318Mbits/s with 192-bit key and 275Mbits/s with 256-bit key on VIRTEX XC4VSX25-12, and the total number of slices is 1766. The proposed reconfigurable and compact AES architecture can be efficiently applied in computing-restricted environments such as wireless and embedded devices.
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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 | 0.000 |
| 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) | 0.003 | 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".