MétaCan
Menu
Back to cohort

ExaNoDe: Combined Integration of Chiplets on Active Interposer with Bare Dice in a Multi-Chip-Module for Heterogeneous and Scalable High Performance Compute Nodes

2020· preprint· en· W3109527654 on OpenAlexaff
Pierre-Yves Martinez, Yann Beilliard, Maxime Godard, David Danovitch, Dominique Drouin, Jean Charbonnier, P. Coudrain, Arnaud Garnier, Didier Lattard, Pascal Vivet, S. Chéramy, Èric Guthmuller, César Fuguet, V. Mengue, Jean Durupt, A. Philippe, Denis Dutoit

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersHorizon 2020 Framework ProgrammeAgence Nationale de la Recherche
KeywordsScalabilityComputer scienceModular designInterposerDiceNode (physics)Computer architectureField-programmable gate arrayContext (archaeology)Parallel computingSupercomputerChipEfficient energy useRouting (electronic design automation)Embedded systemComputational scienceEngineeringMaterials scienceElectrical engineeringNanotechnologyOperating systemLayer (electronics)

Abstract

fetched live from OpenAlex

In the context of high performance computing (HPC), energy efficiency and computing density are key for targeting exascale architectures. Close integration of chiplets, active interposer and field programmable gate arrays (FPGA) paves the way for dense, efficient and modular compute nodes. In this paper, we detail the ExaNoDe multi-chip-module (MCM) combining the integration of a substrate, an active interposer, some chiplets and bare dice. The reported MCM demonstrates that the multi-level integration flow enables tight integration of hardware accelerators in a heterogeneous HPC compute node.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.032
GPT teacher head0.268
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicParallel Computing and Optimization TechniquesFrench-language works237,207