Fast Turnaround HLS Debugging Using Dependency Analysis and Debug Overlays
Bibliographic record
Abstract
High-level synthesis (HLS) has gained considerable traction over recent years, as it allows for faster development and verification of hardware accelerators than traditional RTL design. While HLS allows for most bugs to be caught during software verification, certain non-deterministic or data-dependent bugs still require debugging the actual hardware system during execution. Recent work has focused on techniques to allow designers to perform in-system debug of HLS circuits in the context of the original software code; however, like RTL debug, the user must still determine the root cause of a bug using small execution traces, with lengthy debug turns. In this work, we demonstrate techniques aimed at reducing the time HLS designers spend performing in-system debug. Our approaches consist of performing data dependency analysis to guide the user in selecting which variables are observed by the debug instrumentation, as well as an associated debug overlay that allows for rapid reconfiguration of the debug logic, enabling rapid switching of variable observation between debug iterations. In addition, our overlay provides additional debug capability, such as selective function tracing and conditional buffer freeze points. We explore the area overhead of these different overlay features, showing a basic overlay with only a 1.7% increase in area overhead from the baseline debug instrumentation, while a deluxe variant offers 2×--7× improvement in trace buffer memory utilization with conditional buffer freeze support.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".