Optimal Pattern Retargeting in IEEE 1687 Networks: A SAT-based Upper-Bound Computation
Bibliographic record
Abstract
A growing number of embedded instruments is being integrated into System-on-Chips for testing, monitoring, and several other purposes. To standardize their access protocols, the IEEE 1687 (IJTAG) standard has defined a flexible network infrastructure. Finding the shortest path in such networks requires a comprehensive search over a solution space, bounded by a limited number of time frames. This bound must be selected carefully, as it can neither be too large (to avoid unnecessary long execution time) nor too small (to avoid missing the optimal solution). Previous work was not efficiently applicable to all segments of IJTAG networks, with some providing unrealistic bounds and others having scope limitations or scalability issues. In this work, we present a new methodology for computing the upper-bound on the number of time frames using the Boolean Satisfiability Problem (SAT). Our proposed technique can also be customized to perfectly adapt to instruments access procedures, which in turn increases efficiency by reducing the time spent searching for required configurations. Results show the effectiveness of our work in computing the upper-bound for irregular benchmarks that are not constrained by a specific network design. This is achieved with a controlled increase in execution time, in contrast to previous work.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".