Energy-efficient implementation of AES algorithm on 16nm FPGA
Bibliographic record
Abstract
Cryptographic algorithms ensure security of data in CPSs, IoT and SCADA systems and platforms. Some researchers ascertained that the security processes have extensive effects on battery life of a device and FPGAs present a novel resolution for augmenting the performance of devices and the AES algorithm offers means to secure data transmission. In this research, we have analyzed the power consumption of the AES algorithm on 16nm Kintex Ultrascale+ FPGA for 5 different IO Standards to determine the least power consuming and an energy efficient architecture for its implementation. We have used Xilinx Vivado 2018.2 ISE for all the observations done in this work. Out of 5 IO Standards analyzed, POD12 and HSTL_I_12 IO Standards consumed least power and LVCMOS consumed maximum power. At output load of 10000pF, there is 94.92% savings in total on-chip power utilization when we migrate our design from LVCMOS18 to HSTL_I_12 and 94.88% savings in total on-chip power utilization when we migrate our design from LVCMOS18 to POD12. For further reducing the power consumption, different Green Computing techniques like frequency scaling, thermal scaling, clock gating etc can be applied. We may also execute our work on 3-D and 4-D ICs. The outcomes gained in this paper can assist in a more energy efficient FPGA implementation of AES.
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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 | 0.000 | 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".