MétaCan
Menu
Back to cohort

“Zen 5”: The AMD High-Performance 4nm x86-64 Microprocessor Core

2025· article· en· W4408183445 on OpenAlexaff
Teja Singh, Oliver Spence, Sundar Rangarajan, Shane Southard, Carson Henrion, Alex Schaefer, Brett C. Johnson, Sarah Bartaszewicz Tower, K. Hoover, Deepesh John, Ted Antoniadis, Shravan Lakshman, V. K. Mittal, Brian Kasprzyk, Kurt Mohlman, Hon-Hin Wong, Daryl Lieu, Russell Schreiber, S. K. Singh, Nick Lance, Darryl Prudich, Justin Coppin, Tim Jackson, Anita Karegar, Ryan S. Miller, Sabeesh Balagangadharan, James Pistole, Wilson Li, Michael T. McCabe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsAdvanced Micro Devices (Canada)
Fundersnot available
Keywordsx86MicroprocessorComputer scienceCore (optical fiber)Embedded systemOperating systemTelecommunicationsSoftware

Abstract

fetched live from OpenAlex

Codenamed “Zen 5,” AMD's next-generation, energy-efficient high-performance x86 core targets a wide array of client, server, and embedded markets. Fabricated in TSMC's 4nm FinFET process, the 55mm2 core complex (CCX), shown in Fig. 2.1.1., contains 8.6B transistors across eight cores, each with a 1MB private L2 cache and a shared 32MB L3 cache. The “Zen 5” implementation supports configurable FP256 and FP512 data paths. The “Zen 5” family includes a “Zen 5c” variant with increased density and power efficiency for key markets. The main design priorities for “Zen 5” are to improve per-core performance and energy efficiency, while maintaining similar area footprint as the prior generation [1]. The “Zen 5” core delivers a -16% generational lPC increase in desktop PC applications [2] while supporting frequencies up to 5.7GHz [3].

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicParallel Computing and Optimization TechniquesFrench-language works237,207