Low-Complexity One-Dimensional Parallel Semi-Systolic Structure for Field Montgomery Multiplication Algorithm Perfect for Small IoT Edge Nodes
Bibliographic record
Abstract
The use of IoT technology in several applications is hampered by security and privacy concerns with IoT edge nodes. Security flaws can only be resolved by implementing cryptographic protocols on these nodes. The resource constraints of the edge nodes make it extremely difficult to implement these protocols. The majority of cryptographic protocols’ fundamental operation is finite-field multiplication, and their performance is significantly impacted by their effective implementation. Therefore, this work mainly focuses on implementing low-area with low-energy and high-speed one-dimensional bit-parallel semi-systolic multiplier for the Montgomery multiplication algorithm. The space and delay complexity analysis of the proposed multiplier structure reveals that the proposed design has a significant reduction in delay and a marginal reduction in the area when compared to the competitive one-dimensional multipliers. The obtained ASIC synthesis report demonstrates that the suggested multiplier architecture saves a marginal amount of space as well as a significant amount of time, area–delay product (ADP), and power–delay product (PDP) when compared to the competitive ones. The obtained results indicate that the proposed multiplier layout is very appropriate for use in devices with limited resources such as IoT edge nodes and tiny embedded devices.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".