MCXplore: An Automated Framework for Validating Memory Controller Designs
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".