On Error Injection for NoC Platforms: A UVM-Based Generic Verification Environment
Bibliographic record
Abstract
Error injection has become critically important for testing the reliability of newly designed hardware systems. Evaluating how a design under test (DUT) reacts to different error-injection methodologies is essential for verification engineers to design dependable universal verification methodology (UVM) scoreboards for error-detection purposes. The first main contribution of this paper is to decide on the feasibility and compatibility of some error-injection techniques when used with networks-on-chip (NoC) platforms for simulation and hardware emulation environments. We target a UVM-based error-injection and detection environment with reusable components. Proposed techniques, introducing both positive and negative test scenarios, are applied to two examples of NoC components: 1) a base router and 2) Daniel router. Base router is a simple case study to prove proposed schemes, whereas Daniel router is a complex reconfigurable open-source case study. Daniel router provides the ability to change router architecture with some parameters and applied algorithms. The second main contribution of this paper is to integrate a full UVM environment with various verification approaches. Target approaches include error injection and detection using reusable and generic UVM environment and components for NoC. Network response is inspected according to error type and methodology. Finally, the proposed UVM environment is used to test and verify an N × N 2-D network composed of base routers or Daniel routers.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".