High throughput and area‐efficient FPGA implementation of AES for high‐traffic applications
Bibliographic record
Abstract
This study presents a high throughput field‐programmable gate array (FPGA) implementation of advanced encryption standard‐128 (AES‐128). AES is a well‐known symmetric key encryption algorithm with high security against different attacks that are widely used in different applications. The main goal of this study is to design a high throughput and FPGA efficiency (FPGA‐Eff) cryptosystem for high‐traffic applications. To achieve high throughput, loop‐unrolling, inner and outer pipelining techniques are employed. In AES, substitution bytes (Sub‐Bytes) is one of the costly functions that occupy a large number of resources and has a large delay. To reduce the area of Sub‐Bytes, new‐affine‐transformation, which is the combination of inverse isomorphic and affine transformation, is proposed and employed. Besides that, AES has been modified according to the proposed architecture. For the first nine rounds, Shift‐Rows and Sub‐Bytes have been exchanged, and Shift‐Rows is merged with Add‐Round‐Key. To make an equal latency between stages, Mix‐Columns is divided into two different stages. AES is implemented in counter mode on Xilinx Virtex‐5 using VHDL. The proposed implementation achieves a throughput of 79.7 Gbps, FPGA‐Eff of 13.3 Mbps/slice, and frequency of 622.4 MHz. Compared to the state‐of‐the‐art work, the proposed design has improved data throughput by 8.02% and FPGA‐Eff by 22.63%.
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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) | 0.004 | 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".