Word-Based Processor Structure for Montgomery Modular Multiplier Suitable for Compact IoT Edge Devices
Bibliographic record
Abstract
The Internet of Things (IoT) is an emerging technology that forms a huge network of different objects and intelligent devices. IoT Security is becoming more important due to the exchange of sensitive sensor data and the potential for incorporating the virtual and real worlds. IoT edge devices create serious security threats to network systems. Due to their limited resources, it is challenging to implement cryptographic protocols on these devices to secure them. To address this problem, we should perform compact implementation of cryptographic algorithms on these devices. At the heart of most cryptographic algorithms is the modular multiplication operation. Therefore, efficient implementation of this operation will have a great impact on the implementation of the whole cryptographic protocol. In this paper, we will focus on the resource and energy efficient hardware implementation of the adopted Montgomery modular multiplication algorithm over GF(2m). The main building block of the proposed word-based processor structure is a processor array that has a modular structure with local connectivity between its processing elements. The ability to manage the saving amounts of area, delay, and consumed energy is the main benefit of the suggested hardware structure. We used ASIC technology to implement the suggested word-based processor structure. The final results show an average reduction in the area of 86.3% when compared with the competitive word-based multiplier structures. Additionally, the recommended design achieves significant average savings in area-time product, power, and consumed energy of 53.7%, 83.2%, and 72.6%, receptively, over the competitive ones. The obtained results show that the provided processor structure is best suited for application in compact IoT edge devices with limited resources.
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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) | 0.004 | 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".