system-level modeling of programmable packet \nprocessing systems
Bibliographic record
Abstract
Computer networks are experiencing explosive growth which is reinforced by the recent \nexhaustion of the global IPv4 addresses space in 2011 and the tenfold increase in users from 1999 \nto 2013. The advent of cloud, mobile and IoT is only going to accelerate this growth. This accedes \nthe need for flexible and scalable networks that process packets faster. Programmable packet \nprocessing systems have emerged as a solution which aim to find balance between flexibility of \nsupporting different processing functions while maintaining a high processing capability. \nDesigning architectures that support such paradigms is fairly complicated as decisions need to be \nmade for evaluating trade-offs between flexibility and efficiency. Questions like what \nprogrammatic interfaces, services, applications and protocols are required need to be answered \nbefore synthesis of actual hardware. To evaluate such requirements modelling techniques are \nrequired to evaluate architecture decisions accurately early enough in the design phase. \nIn this thesis, we propose a flexible system level modelling methodology for early \nvalidation, design and analysis of packet processing applications for programmable forwarding \nplane architectures. The hardware and software architecture is described in a high level language \nwhich can be used to describe forwarding planes from many core network processors to \nreconfigurable processing pipelines. Device architects can use this for design space exploration, \nprototyping and validation; where application developers can start pre-silicon application design, \ndevelopment and debugging to evaluate different hardware and software decisions in an industry \nwith ever shrinking market windows.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".