Compact Word-Serial Modular Multiplier Accelerator Structure for Cryptographic Processors in IoT Edge Nodes with Limited Resources
Bibliographic record
Abstract
IoT is extensively used in many infrastructure applications, including telehealth, smart homes, smart grids, and smart cities. However, IoT has the weakest link in system security since it often has low processing and power resources. It is important to implement the necessary cryptographic primitives in these devices using extremely efficient finite field hardware structures. Modular multiplication is the core of cryptographic operators. Therefore, we present, in this work, a word-serial modular multiplier accelerator structure that provides the system designer with the ability to manage areas, delays, and energy consumption through selecting the appropriate embedded processor word size l. The modularity and regularity of the suggested multiplier structure makes it more suitable for implementation in ASIC technology. The ASIC implementation results indicates that the offered multiplier structure achieves area reduction compared to the competitive existing multiplier structures that vary from 76.2% to 98.5% for l=8, from 73.1% to 98.1% for l=16, and from 82.9% to 98.3% for l=32. Moreover, the energy reduction varies from 61.2% to 98.8% for l=8, from 67.7% to 98.3% for l=16, and from 76.1% to 98.8% for l=32. These results indicate that the proposed modular multiplier structure significantly outperforms the competitive ones, in terms of area and consumed energy, making it more suitable for utilization in resource-constrained IoT edge devices.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".