Design of an optimal test access architecture using a genetic algorithm
Bibliographic record
Abstract
Test access is a major problem for core-based system-on-chip (SOC) designs. Since cores in an SOC are not directly accessible via chip inputs and outputs, special access mechanisms are required to test them at the system level. One of the most important issues in designing a test access architecture is testing time. Here, several issues related to the design of an optimal test access architecture with the goal of minimizing testing time are discussed. These issues include the assignment of cores to test buses, the distribution of test data width between multiple test buses, and the estimation of test data requirements to satisfy an upper bound on the testing time. Previous works show that all of these problems are NP-complete. Here, we applied a genetic algorithm (GA) to solve these problems. Experiments were run on two hypothetical but non-trivial SOCs using the implemented GA. The results show a 40% improvement. The performance improvement is principally due to our removing the constraints of the necessity of serialization and allowing the system to handle serial or parallel test data loading for any core.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".