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Non-Volatile Phase Change Material based Nanophotonic Interconnect

2022· article· en· W4221156338 on OpenAlexafffund
Parya Zolfaghari, Joseph Ortiz, Cédric Killian, Sébastien Le Beux

Bibliographic record

Venue2022 Design, Automation & Test in Europe Conference & Exhibition (DATE) · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPhase-change materials and chalcogenides
Canadian institutionsConcordia University
FundersFonds de recherche du Québec – Nature et technologiesAgence Nationale de la Recherche
KeywordsNanophotonicsInterconnectionComputer scienceOptoelectronicsPhase changeMaterials scienceTelecommunicationsEngineering physicsPhysics

Abstract

fetched live from OpenAlex

Integrated optics is a promising technology to take advantage of light propagation for high throughput chip-scale interconnects in many core architectures. A key challenge for the deployment of nanophotonic interconnects is their high static power, which is induced by signal losses and devices calibration. To tackle this challenge, we propose to use Phase Change Material (PCM) to configure optical paths between writers and readers. The non-volatility of PCM elements and the high contrast between crystalline and amorphous phase states allow to bypass unused readers, thus reducing losses and calibration requirements. We evaluate the efficiency of the proposed PCM-based interconnects using system level simulations carried out with SNIPER manycore simulator. For this purpose, we have modified the simulator to partition clusters according to executed applications. Simulation results show that bypassing readers using PCM leads up to 52% communication power saving.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0750.003

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 head0.102
GPT teacher head0.306
Teacher spread0.205 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes2
Has abstractyes

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