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Record W2346166411 · doi:10.3850/9783981537079_0490

MCXplore: An Automated Framework for Validating Memory Controller Designs

2016· article· en· W2346166411 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceCorrectnessDramConstruct (python library)Set (abstract data type)Test suiteModel checkingRegression testingDynamic random-access memoryMemory controllerEmbedded systemProgramming languageTest caseOperating systemComputer hardwareSoftwareMachine learningSoftware developmentRegression analysisSemiconductor memory

Abstract

fetched live from OpenAlex

This work presents an automated framework for the validation of dynamic random access memory controllers (DRAM MCs) called MCXplore. In developing this framework, we construct formal models for memory requests interrelation and DRAM command interaction. The framework enables validation engineers to define their test plans precisely as temporal logic specifications. We use the NuSMV model-checker to generate counter-examples that serve as test templates; hence, MCXplore uses these test templates to generate memory tests to validate the correctness properties of the memory controller. We show the effectiveness of MCXplore by validating various state-of-the-art MC features as well as hard-to-detect timing violations that often occur. We also provide a set of predefined test plans, and regression tests that validate essential properties of modern DRAM MCs. We release MCXplore as an open-source framework to allow validation engineers and researchers to extend and use.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.736
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.120
GPT teacher head0.390
Teacher spread0.269 · 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

Quick stats

Citations9
Published2016
Admission routes1
Has abstractyes

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