QuT: A low-power optical Network-on-Chip
Bibliographic record
Abstract
To enable the adoption of optical Networks-on-Chip (NoCs) and allow them to scale to large systems, they must be designed to consume less power and energy. Therefore, optical NoCs must use a small number of wavelengths, avoid excessive insertion loss and reduce the number of microring resonators. We propose the Quartern Topology (QuT), a novel low-power all-optical NoC. We also propose a deterministic wavelength routing algorithm based on Wavelength Division Multiplexing that allows us to reduce the number of wavelengths and microring resonators in optical routers. The key advantages of QuT network are simplicity and lower power consumption. We compare QuT against three alternative all-optical NoCs: optical Spidergon, λ-router and Corona under different synthetic traffic patterns. QuT demonstrates good scalability with significantly lower power and competitive latency. Our optical topology reduces power by 23%, 86.3% and 52.7% compared with 128-node optical Spidergon, λ-router and Corona, respectively.
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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 | 0.000 | 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) | 0.001 | 0.001 |
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".