A New 16-Bit IoT ASIC Design for the AES Encryption Algorithm
Bibliographic record
Abstract
Previous works to secure IoT devices have mainly focused on 8-bit hardware architectures for AES encryption. In this paper, we present a new 16-bit ASIC design for AES encryption optimized for IoT systems. Our design includes a new 16-bit key derivation circuit that generates keys dynamically in parallel with the datapath, enhancing security by avoiding key exposure and protecting against existing attacks. Our design employs column-wise byte ordering for both the datapath and key derivation, eliminating the need for external reordering and reducing hardware resource usage. Additionally, we design a lightweight 16-bit serial MixColumns circuit that supports higher data rates compared to existing designs. ASIC implementation results using a 65nm CMOS technology library demonstrate a 50% increase in throughput with a 21% increase in area over previous 8-bit based designs. Our lightweight and fast AES ASIC design offers a tailored solution for securing IoT systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".