A RAM-based generic packet switch with scheduling capability
Bibliographic record
Abstract
A generic hardware solution is introduced for switching variable-length packets. The switch can be used in a multiprotocol environment including IP and ATM protocols. In this architecture the packets and/or cells are stored in a shared RAM memory as the main storage resource in the switch, and similar to RAM-based, shared-buffer ATM switch, for each packet a fixed-length minicell is given to a queue controller which provides queueing and scheduling. A bank of sequencer circuits can be used as the controller. We introduce a new version of the single-queue switch, as a compact controller to replace the bank of sequencers with a single sequencer, with the same speed and size. Fair queueing scheduling is implemented by the controller. Other schemes can also be implemented using the sequencer circuit, such as priority queueing. Plug-in modules handle the link and network (routing) protocols. Therefore, these modules can be easily changed or replaced. Multiple protocol modules are also possible in this way. High speed switching is possible in this architecture because the original packets and cells and also the minicells are handled by hardware. The hardware also provides a multicasting capability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".