<title>Error- and flow-control protocols for terabit optical networks</title>
Bibliographic record
Abstract
The design of an optical image guide network for distributed multiprocessing is described. The network supports multiple high bandwidth rings between workstations over distances of 10s of meters. Traditionally, error and flow control functions for multiprocessor networks are implemented in custom high speed electronic Application Specific Integrated Circuits which are physically removed from the interconnect's physical layer. In this paper, we consider migrating these functions directly into the optoelectronic physical layer, yielding an 'Intelligent Optical Network'. Conventional error control protocols are infeasible with dense bit parallel optical systems based on image guides since they require excessive amounts of hardware. The designs of efficient error and flow control protocols for such networks are proposed and analyzed. The key blocks of the protocols have been designed, fabricated and demonstrated in 0.8 micron and 0.5 micron CMOS/SEED devices. The protocols require significantly less hardware then alternative schemes, and CMOS/VCSEL devices supporting these protocols are scalable to very high bandwidths, i.e., 10s of Terabits per second.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".