QUEUE MODELING AND IMPLEMENTATION FOR NETWORKS-ON-CHIP ROUTERS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Queue modeling is an important step in Networks-on-Chip (NoC) design to understand and estimate the system behavior at early design phases. Choosing queue parameters, such as queue size, maximum packet arrival rate, packet service rate, directly impacts the performance and silicon area of the overall NoC-based design. In this paper, we propose a new 2D M/D/1/B queuing model for NoC routers. Using our model, we prove that packet service rate impacts throughput significantly. On the other hand, changing the queue size, within acceptable ranges for NoC applications, does not have a noticeable effect on the throughput. Through a case study implementation on FPGA, we explain how this model could be used in different applications to obtain design parameters at higher levels of abstraction. Synthesis and performance analysis are performed to validate the proposed model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it