Probability-based mapping approach for an application-aware networks-on-chip architectures
Bibliographic record
Abstract
In a digital and automation era, on-chip multi-core architecture plays a vital role in effective communication in the field of very large-scale integrated circuits (VLSI). In this paper, we propose a unique mapping approach in which a probability-based core selection from the application benchmark into the center to eccentric way of placement of cores in the standard network architecture improves the performance of networks-on-chip (NoC). The proposed approach utilizes a structured mapping strategy, in contrast to the random mapping. This characteristic renders the proposed method a robust solution for a diverse range of NoC architectures irrespective of scale. The proposed approach provides better quality of service (QoS) with optimal total communication bandwidth and average hop count . The performance of the proposed mapping approach is validated with various experiments over standard and real-time benchmarks. The investigation results indicate that the total communication cost over real-time NoC benchmarks for the proposed mapping approach offers 43.06%, 22.75%, and 16.69% average improvement over CastNet, NMAP, and mapGtoM respectively. Furthermore, we adopt uniform geometric and shuffled traffic patterns to identify the latency and throughput of the proposed probability-based mapping approach. The investigation results indicate that the proposed mapping approach outperforms existing mapping procedures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".