Energy Efficiency Analysis and Implementation of AES on an FPGA
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
I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. The Advanced Encryption Standard (AES) was developed by Joan Daemen and Vincent Rjimen and endorsed by the National Institute of Standards and Technology in 2001. It was designed to replace the aging Data Encryption Standard (DES) and be useful for a wide range of applications with varying throughput, area, power dissipation and energy consumption requirements. Field Programmable Gate Arrays (FPGAs) are flexible and reconfigurable integrated circuits that are useful for many different applications including the implementation of AES. Though they are highly flexible, FPGAs are often less efficient than Application Specific Integrated Circuits (ASICs); they tend to operate slower, take up more space and dissipate more power. There have been many FPGA AES implementations that focus on obtaining high throughput or low area usage, but very little research done in the area of low power or energy efficient FPGA based AES; in fact, it is rare for estimates on power dissipation to be made at all.
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 | 0.001 |
| 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