Coherent crosstalk noise analyses in ring-based optical interconnects
Bibliographic record
Abstract
Recently, optical interconnects have been proposed for ultra-high bandwidth and low latency inter/intra-chip communication in multiprocessor systems-on-chip (MPSoCs). These optical interconnects employ the microresonators (MRs) to direct/detect the optical signal. However, utilized MRs suffer from intrinsic crosstalk noise and power loss, degrading the network efficiency via the signal-to-noise ratio (SNR). In this paper, both coherent and incoherent crosstalk in wavelength-division multiplexing (WDM) networks are discussed and systematically analyzed. We carefully develop our analytical models at the optical-circuit level, and apply them to two ring-based networks: SUOR and Corona ONoCs. The quantitative results have demonstrated that the architectural design of the ONoCs determines the impact of crosstalk on the SNR. Even though SUOR and Corona are both ring-based ONoCs, the worst-case SNR can be differed up to 50dB. Our analyses of the worst-case SNR can be utilized as a platform to compare the realistic performance among different optical interconnection networks via the degradation of BER and data bandwidth.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".