A High-Speed FPGA Implementation of AES for Large Scale Embedded Systems and its Applications
Bibliographic record
Abstract
In this paper, a high-speed hardware implementation of the AES encryption algorithm is presented. targeting the large scale and high bandwidth embedded systems and applications. The high-speed implementation is developed by employing the pipelining architectural technique, where the fully sub pipelined architecture is applied efficiently. The applied architecture is performed using the AES 128-bit data path. The fully sub pipelined architectural technique is implemented by constructing the Sboxes of the AES encryption algorithm using the BRAMs of the FPGA device. The pipelined design is realized on the new Xilinx FPGA devices, Artix-7, Virtex-7, Kintex-7, Spartan-7, and Kintex UltraScale. The proposed design is fully synthesized, translated, placed, and routed using the new Xilinx Vivado 2020 design suite. The hardware implementation results of the proposed pipelined design show an efficiency in terms of speed, utilized resources, and throughput. Comparing with previous works, our proposed pipelined design utilizes less FPGA resources, operates at high operating frequencies, and delivers high throughputs.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".