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Record W4234208104 · doi:10.1109/dac.2003.1219120

A retargetable microarchitecture simulator

2004· article· en· W4234208104 on OpenAlexaff
Wai Sum Mong, Jianwen Zhu

Bibliographic record

VenueProceedings 2003. Design Automation Conference (IEEE Cat. No.03CH37451) · 2004
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicroarchitectureComputer scienceComputer architectureMicroprocessorInstruction setEmbedded systemBranch predictorComputer architecture simulatorParallel computing

Abstract

fetched live from OpenAlex

The capability of performing architectural exploration has become essential for embedded microprocessor design in System-On-Chip. While many retargetable instruction set (ISA) simulators have been reported, the more relevant microarchitecture simulators, which are capable of modeling the detailed machine features such as cache organization, branch prediction and out-of-order scheduler, have not been equipped with retargetability. In this paper, we propose a new methodology that can generate completed microarchitecture simulators from the abstract ISA and the application binary interface (ABI) specification. We demonstrate our methodology by the development of a tool that can automatically port the SimpleScalar toolset, the de facto standard for microarchitecture simulation, to any processor.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.599
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.025
GPT teacher head0.246
Teacher spread0.221 · 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.

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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

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