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Record W2107495642 · doi:10.1109/isscc.2011.5746227

Design solutions for the Bulldozer 32nm SOI 2-core processor module in an 8-core CPU

2011· article· en· W2107495642 on OpenAlexaff
Tim Fischer, Srikanth Arekapudi, Eric Busta, C. Dietz, Michael Golden, S. Hilker, Atsushi Horiuchi, Kate Hurd, D. Johnson, H. McIntyre, Samuel Naffziger, James Vinh, Jonathan M. White, Karen S. Wilcox

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLow-power high-performance VLSI design
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
KeywordsMulti-core processorComputer scienceFLOPSEmbedded systemCore (optical fiber)Low-power electronicsTransistorSilicon on insulatorParallel computingPower (physics)Power consumptionEngineeringElectrical engineeringTelecommunicationsPhysicsVoltageOptoelectronicsSilicon

Abstract

fetched live from OpenAlex

AMD's 2-core "Bulldozer" module contains 213 million transistors in an 11 metal layer 32nm HKMG SOI CMOS process and is designed to operate from 0.8 to 1.3V. This new micro-architecture improves performance and frequency while reducing area and power compared to a previous AMD x86-64 CPU in the same process. To achieve these goals, the design reduced the number of F04 inverter delays/cycle by more than 20%, achieving higher frequencies in the same power envelope even with increased core counts. The 2-core CPU module area (including 2MB L2 cache) is 30.9mm2. The Bulldozer micro-architecture is cycle-based, using soft-edge flip-flops (SEF) to provide high-frequency performance, process variation tolerance, and low power consumption.

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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.131
GPT teacher head0.253
Teacher spread0.122 · 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

Citations44
Published2011
Admission routes1
Has abstractyes

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