Power and scalability analysis of multi-plane optical interconnection networks
Bibliographic record
Abstract
The scalability of current electrical interconnection networks will be soon limited by their power consumption and dissipation. To overcome such an issue, multi-plane optical interconnection networks have been proposed. Multi-plane networks are composed of a number of cards, each of them supporting a number of ports, interconnected through a passive optical backplane. Two switching domains are envisioned to be exploited allowing for flexible switching of data packets across all ports and cards. This study considers three different implementations that are representative of the multi-plane optical interconnection networks based on space and wavelength switching. The implementations differ from each other in the switching domain used. The aim of the work is to investigate the power consumption and scalability of the three implementations, based on state-of-the-art 40 Gb/s technology. The physical layer analysis and the power consumption assessment including both electronic and optical components indicate that the implementation can impact on the scalability as well as the power consumption. The arrayed waveguide gratings (AWG)-based implementation is found to suffer from scalability and power consumption issues. On the other hand, an implementation based on active switches shows an energy efficiency similar to the coupler-based implementation, but a four-fold scalability increase.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".