Efficient Power Estimation Using DSENT for 3D-Mesh on Chip Optic Communication Network
Bibliographic record
Abstract
The Network-on-Chip (NoC) stands as a potent solution for achieving heightened performance, efficient communication, and dependability in the migration of Very-Large Scale Integration (VLSI) architecture toward deep submicron technology, when contrasted with conventional connectivity networks.Considerable research endeavours have been allocated to diverse facets of NoCs, encompassing topology, routing algorithms, traffic behaviours, power management, and fundamental mapping.This paper explores the power consumption efficiency of Parameterized Path-Based, Randomized, Oblivious, Minimal for 3D Mesh (PROM3D) routing and ZXY routing algorithms for various traffic patterns like transpose, bit shuffle, and random traffic with the help of the integrated DSENT network model.The PROM3D routing algorithm selects a path randomly from all possible minimal pathways between the source and destination, whereas ZXY is a layer-based routing method.The Design Space Exploration of Network (DSENT) tool is used with the NoC Interconnect Routing and Applications Modeling (NIRGAM) simulator in experiments to measure the power consumption.The findings indicate that, within the 3D-Mesh environment, the ZXY routing algorithm exhibits a 0.02% of variation in power consumption while in saturation on varying loads for various traffic patterns in comparison to the PROM3D algorithm.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".