Design of an optimal test access architecture using a genetic algorithm
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it