Time Domain Multiplexed TAM: Implementation and Comparison
Bibliographic record
Abstract
One of the difficult problems which core-based systemon -chip (SoC) designs face is test access. For testing the cores in a SoC, a special mechanism is required, since they are not directly accessible via chip inputs and outputs. In this paper we introduce a novel Test Access Mechanism (TAM) based on time domain multiplexing (TDM-TAM). This TAM is P1500 compatible and uses a P1500 wrapper. The TAM characteristics are its flexibility, scalability, and reconfigurability. The proposed TAM is compared with two other approaches: a serial threading approach analogous to the IEEE1149.1 standard (Serial TAM)[7]and a packetswitching test network (NIMA)[9]. A network-processing engine SoC is used as a platform to compare the different TAMs [6]. Results show that in most cases, TDM is the most effective TAM in both test time and overhead area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".