Capturing the sensitivity of optical network quality metrics to its network interface parameters
Bibliographic record
Abstract
SUMMARY Optical networks‐on‐chip (ONoCs) are gaining momentum as a way to improve energy consumption and bandwidth scalability in the next generation multicore and many‐core systems. Although many valuable research works have investigated their properties, the vast majority of them lack an accurate exploration of the network interface architecture required to support optical communications on the silicon chip. The complexity of this architecture is especially critical for a specific kind of ONoCs: the wavelength‐routed ones. These are capable of delivering contention‐free all‐to‐all connectivity without the need for path reservation, unlike space‐routed ONoCs. From a logical viewpoint, they can be considered as full nonblocking crossbars; thus, the control complexity is implemented at the network interfaces. To our knowledge, this paper proposes the first complete network interface architecture for wavelength‐routed optical NoCs, by coping with the intricacy of networking issues such as flow control, buffering strategy, deadlock avoidance, serialization, and above all, their codesign in a complete architecture. The evaluation methodology spans from area and energy analysis via actual synthesis runs in 40‐nm technology to RTL‐equivalent (register‐transfer level) SystemC modelling of the network architecture and aims at verifying whether the projected benefits of ONoCs versus their electrical counterparts are still preserved when the complexity of their network interface is considered in the analysis. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.004 |
| 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) | 0.001 | 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".