Congestion aware adaptive routing for network-on-chip communication
Bibliographic record
Abstract
Network-On-Chip (NoC) has surpassed the traditional bus based on-chip communication in offering better performance for data transfers among many processing, peripheral and other cores of high performance embedded systems. Adaptive routing provides an effective way of efficient on-chip communication among NoC cores. The message routing efficiency can further improve the performance of NoC based embedded systems on a chip. Congestion awareness has been applied to adaptive routing for achieving better data throughput and latency. This thesis presents a novel approach of analyzing congestion to improve NoC throughput by improving packet allocation in NoC routers. The routers would have the knowledge of the traffic conditions around themselves by utilizing the congestion information. We employ header flits to store the congestion information that does not require any additional communication links between the routers. By prioritizing data packets that are likely to suffer the worst congestion would improve overall NoC data transfer latency.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".