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Record W2160684608 · doi:10.1109/mppoi.1995.528623

Design of a terabit free-space photonic backplane for parallel computing

2002· article· en· W2160684608 on OpenAlexafffund
Ted H. Szymanski, H.S. Hinton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsMcGill University
FundersMcGill University
KeywordsBackplaneTerabitComputer sciencePhotonicsBandwidth (computing)Computer hardwareMultiplexingPixelWavelength-division multiplexingOptoelectronicsComputer networkTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

The design of a terabit free-space photonic backplane for parallel computing and communications is described. The backplane consists of a large number of parallel reconfigurable optical channels spaced a few hundred microns apart. The parallel channels are organized as a unidirectional ring and the channel access protocols are implemented by smart pixel arrays. Smart pixel arrays are integrated optoelectronic devices with optical I/O and with electronic processing capabilities. The design of a 32/spl times/32 smart pixel array which supports multiple reconfigurable broadcast channels and interfaces between tens of Gb/s of electrical data and hundreds of Gb/s of optical data is proposed. The photonic backplane interconnects 32 printed circuit boards (PCBs) and has a bisection bandwidth of 1 terabit/sec (Tb/s), with each PCB receiving a bandwidth of 32 Gb/s. The backplane can be dynamically reconfigured to support 1024 broadcast channels at 1 Gb/s, 32 broadcast channels at 32 Gb/s, or many intermediate values. The backplane can also embed arbitrary graphs, including meshes, hypercubes, shuffles, etc. Smart pixel arrays are currently being fabricated using AT&T's Hybrid SEED process, and a demonstration of the architecture interconnecting 4 PCBs is planned for the fall of 1995.

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)0.000
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 score0.854
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.216
Teacher spread0.190 · 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

Citations21
Published2002
Admission routes2
Has abstractyes

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