Simulation of an Integrated Architecture for IP-over-ATM Frame Processing
Bibliographic record
Abstract
The performance of an integrated architecture for full-duplex IP-over-ATM processing is evaluated through detailed simulation. The architecture combines processing, memory, and multiple direct-memory-access engines for single-chip implementation. The simulation models the segmentation and reassembly operations needed to translate IP frames to and from a fixed ATM cell size. A key operation is the insertion of a virtual path and virtual channel identifier (VPI/VCI) into the outgoing ATM cells. Software-based VPI/VCI insertion provides flexibility but requires the on-chip processor to perform this function. Hardware-based VPI/VCI insertion is an optimization that requires one of the direct-memory-access engines to perform this task. The two approaches are evaluated through simulated execution of representative control software with detailed modeling of all on-chip components. Results indicate that software-based VPI/VCI insertion supports full-duplex traffic at 475 Mbps on a 500-MHz processor and that hardware-based VPI/VCI insertion supports full-duplex traffic at 560 Mbps on a 500-MHz processor.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".