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Record W2169258566 · doi:10.1145/775832.776045

Design of a 17-million gate network processor using a design factory

2003· article· en· W2169258566 on OpenAlexaboutno aff
Gilles-Eric Descamps, Satish Bagalkotkar, Subramanian Ganesan, Satish Iyengar, Alain Pirson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsnot available
Fundersnot available
KeywordsNetwork processorComputer scienceThroughputFactory (object-oriented programming)Key (lock)Embedded systemTransistorState (computer science)Field-programmable gate arrayNetwork packetComputer architectureElectrical engineeringEngineeringOperating systemComputer network

Abstract

fetched live from OpenAlex

Silicon Access Networks taped out in one year four high performance SoC products: a high-end Network Processor and three associated Co-processors, providing the industry with the highest performance OC-192 Data Plane Processing solution. The four chips are shipping for revenue and went into production from first silicon with no mask change. They were designed using state-of-the-art 0.13μm technology and collectively represent about 750-million transistors, implementing a variety of analog, digital, high-speed memory and functional blocks. This contribution describes the design of the Packet Processor and some of the key aspects of Silicon Access Networks' design methodology that enabled to accomplish repeatable "first pass silicon" successes, despite system complexity challenges. The 175-million transistor iPP was simultaneously designed in three locations (San Jose/CA, Raleigh/NC, Ottawa/Canada). Bring-up and pre-production showed that first silicon met all its targets: power, speed, yield and complete functionality.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.805
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.260
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2003
Admission routes1
Has abstractyes

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