Bibliographic record
Abstract
This paper focuses on designing a large N/spl times/N high performance, broadband ATM switch. Despite advances in architectural designs, practical switch dimensions continue to be severely limited by both the technological and physical constraints of packaging. Here, we focus on augmentation in a "single-switch" design: we provide ways to construct arbitrarily large switches out of modest-size components and retain overall delay/throughput performance. We propose a growable switch architecture based on several key principles: (1) the knockout principle exploits the statistical behavior of cell arrivals and thereby reduces the interconnect complexity, (2) output queueing yields the best possible delay/throughput performance, (3) distributed control in routing (multicast) cells through the interconnect fabric without internal path conflicts and (4) simple basic building blocks facilitate scalability. Other attractive features of the proposed architecture include: (1) intrinsic broadcast and multicast capabilities, (2) built-in priority sorting functionality and (3) the guarantee of first-in-first-out cell sequence. To achieve 10/sup -14/ cell loss probability, only maximum-size 32/spl times/16 basic building modules are required, and no cross-over interconnects exist between modules in a three-dimensional configuration.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".