Retiming, resynthesis, and partitioning for the pseudo-exhaustive testing of sequential circuits
Bibliographic record
Abstract
In pseudo-exhaustive testing, the partitioning technique consists of placing segmentation cells in an acyclic sequential circuit in order to reduce the size of cones. These segmentations, which are transparent in normal mode and active during test mode, result in an overhead for the circuit. However, cutting edges containing registers eliminates these secondary effects. In this paper, we present a new approach to the partitioning problem of the synchronous sequential circuits based on the retiming technique. This approach consists in choosing a set of segmentation edges such that there exists a retiming minimizing the number of segmentation cells in the retimed circuit. Thus, for a given size limit of cone, we propose an iterative algorithm for pseudo-exhaustive sequential testing which combines our method with existing approaches. In addition, we prove that the proposed retiming can be considered as a peripheral retiming while at the same time integrating the logic optimization part in the partitioning process. Experimental results on the benchmark sequential circuits show that our approach significantly optimizes the retimed circuit and reduces the number of segmentation cells required in the original circuit.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".