Probability-based mapping approach for an application-aware networks-on-chip architectures
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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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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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 it