Compact hardware accelerator for field multipliers suitable for use in ultra-low power IoT edge devices
Why this work is in the frame
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Bibliographic record
Abstract
Adoption of IoT technology without considering its security implications may expose network systems to a variety of security breaches. In network systems, IoT edge devices are a major source of security risks. Implementing cryptographic algorithms on most IoT edge devices can be difficult due to their limited resources. As a result, compact implementations of these algorithms on these devices are required. Because the field multiplication operation is at the heart of most cryptographic algorithms, its implementation will have a significant impact on the entire cryptographic algorithm implementation. As a result, in this paper, we propose a small hardware accelerator for performing field multiplication on edge devices. The hardware accelerator is primarily composed of a processor array with a regular structure and local interconnection among its processing elements. The main advantage of the proposed hardware structure is the ability to manage its area, delay, and consumed energy by choosing the appropriate word size l. We implemented the proposed structure using ASIC technology and the obtained results attain average savings in the area of 95.9%. Also, we obtained significant average savings in energy of 63.2%. The acquired results reveal that the offered hardware accelerator is appropriate for usage in resource-constrained IoT edge devices.
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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