Word-Based Systolic Processor for Field Multiplication and Squaring Suitable for Cryptographic Processors in Resource-Constrained IoT Systems
Bibliographic record
Abstract
Internet of things (IoT) technology provides practical solutions for a wide range of applications, including but not limited to, smart homes, smart cities, intelligent grid, intelligent transportation, and healthcare. Security and privacy issues in IoT are considered significant challenges that prohibit its utilization in most of these applications, especially relative to healthcare applications. Cryptographic protocols should be applied at the different layers of IoT framework, especially edge devices, to solve all security concerns. Finite-field arithmetic, particularly field multiplication and squaring, represents the core of most cryptographic protocols and their implementation primarily affects protocol performance. In this paper, we present a compact and combined two-dimensional word-based serial-in/serial-out systolic processor for field multiplication and squaring over GF(2m). The proposed structure features design flexibility to manage hardware utilization, execution time, and consumed energy. Application Specific Integrated Circuit (ASIC) Implementation results of the proposed word-serial design and the competitive ones at different embedded word-sizes show that the proposed structure realizes considerable saving in the area and consumed energy, up to 93.7% and 98.2%, respectively. The obtained results enable the implementation of restricted cryptographic primitives in resource-constrained IoT edge devices such as wearable and implantable medical devices, smart cards, and wireless sensor nodes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".