High-speed optical packet processing technologies based on novel optoelectronic devices
Bibliographic record
Abstract
To cope with the explosive growth of IP traffic, we must increase both the link capacity between nodes and the node throughput. These requirements have stimulated research on photonic networks that use optical technologies. Optical packet switching (OPS) is an attractive solution because it maximizes the use of the network bandwidth. The key functions in achieving such networks include synchronization, label processing, compression/decompression, regeneration, and buffering for high-speed asynchronous optical packets. However, it is impractical to implement such functions by using all-optical approaches. We have proposed a new optoelectronic system composed of a packet-by-packet optical clock-pulse generator (OCG), an all-optical serial-to-parallel converter (SPC), a photonic parallel-to-serial converter (PSC), and CMOS circuitry. The OCG provides a single optical pulse synchronized with the incoming packet, and the SPC carries out a parallel conversion of the incoming packet. The parallel converted data are processed in the smart CMOS circuit, and reconstructed into an optical packet by the photonic PSC. Our system makes it possible to carry out various functions for high-speed asynchronous optical packets. This paper reviews our recent work on high-speed optical packet processing technologies such as buffering, packet compression/decompression, and label swapping, which are key technologies for constructing future OPS networks.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".