A Case Study of Signal-to-Noise Ratio in Ring-Based Optical Networks-on-Chip
Bibliographic record
Abstract
Microresonators have been utilized to construct optical interconnection networks. One of the drawbacks of these microresonators is that they suffer from intrinsic crosstalk noise and power loss, resulting in Signal-to-Noise Ratio (SNR) reduc-tion and system performance degradation at the network level. The novel contribution of this paper is to systematically study the worst-case crosstalk noise and SNR in a ring-based ONoC, the Corona. In the paper, Corona's data channel and broadcast bus are investigated, with formal general analytical models presented at the device and network levels. Leveraging our detailed analytical models, we present quantitative simulations of the worst-case power loss, crosstalk noise, and SNR in Corona. Moreover, we compare the worst-case results in Corona with those in mesh-based and folded-torus-based ONoCs, all of which consist of the same number of cores as Corona. The quantitative results demonstrate the damaging impact of crosstalk noise and power loss in Corona: the worst-case SNR is roughly 14.0 dB in the network, while the worst-case power loss is substantially high at -69.3 dB in the data channel.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".