An implementation of domain specific languages to microprocessor's Memory Built in Self Repair testing
Bibliographic record
Abstract
Memory Built in Self Repair (MBISR) test in microprocessor testing has always been a very challenging. The main challenges are the complexity of the memory structure and the device to device design variations. Because of this complexity, separate groups with different focus are needed to address the challenge. The test engineers are the experts on Automated Test Equipment (ATE) platforms, while the IP owners are the experts on particular microprocessor's IP. To minimize the test program development and maintenance costs, the test engineers aim to provide a generic solution for all devices. Therefore, to cater for the variations, XML (Extensible Markup Language) has been used to represent the product specific definitions and configurations which will be maintained by the IP owners. However, the surge of new microprocessor designs and more advance innovation to the memory IPs lead to device to device variations increase. In the other hand XML is too static to handle these variations increase and has limited capability to express higher-order structures like conditionals. Therefore a domain specific language (DSL) is adapted to effectively deal with this issue. DSL as opposed to XML is a programming language which provides flexibility as offered by the general purpose languages, such as Java and C++. Yet, it is targeted to a particular kind of problem with its purpose of having separation of business and technical aspect, making it concise and easy to understand by the domain specialists. Therefore DSL fits perfectly as an easy to use language for the IP owners to express freely the product specifications. This paper showcases an example of DSL based solution to MBISR testing in microprocessor.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".