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AMD Instinct™ MI300X Accelerator: Packaging and Architecture Co-Optimization

2024· article· en· W4401881165 on OpenAlexaff
Alan Smith, Gabriel H. Loh, John Wuu, Samuel Naffziger, Tyrone Huang, H. McIntyre, R. Mangaser, Wonjun Jung, Raja Swaminathan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsInstinctArchitectureComputer scienceSystems engineeringEngineeringArtVisual arts

Abstract

fetched live from OpenAlex

The AMD Instinct™ MI300X accelerator utilizes multiple advanced packaging technologies for a heterogeneous integration solution for emerging AI/ML and HPC workloads. These include microbump 3D memory stacks, 2.5D silicon interposers, and 3D hybrid bonding. The combination of these advanced packaging technologies enabled architectural innovations and generational performance uplifts that cannot be achieved with traditional technologies and Moore's Law scaling A modular 3D chiplet design also enables reuse of the MI300X components to enable the MI300A APU.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

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