High throughput and area‐efficient FPGA implementation of AES for high‐traffic applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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%.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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 it