Quantifying observability for in-system debug of high-level synthesis circuits
Bibliographic record
Abstract
In recent years high-level synthesis (HLS) has seen considerable attention as it promises to increase designer productivity and make custom hardware implementation accessible to software developers. A challenge facing those developing HLS technologies is how to allow users to understand, debug and optimize their final hardware systems. Recently, several techniques have been developed to provide in-system debugging capabilities for HLS circuits. These techniques instrument the user's design with some debugging circuitry to provide observability into the circuit during execution. Due to resource constraints, it is usually infeasible to view all variable values for the entire circuit execution. Rather, instrumentation usually captures only some variable values and for only a portion of the circuit execution. In this paper we present a metric for measuring the observability into an executing HLS circuit. This metric reflects the portion of variable accesses that are available to the user, the duration of execution for which these values are available, as well as accommodating variations in importance between source code variables. This metric can be used to understand how different circuit observation networks can provide the user with different levels of observability into the HLS circuit execution. As a demonstration of the applicability of the metric, we first study differences between recent debugging approaches for HLS circuits, and quantify the level of observability provided by such architectures. We then explore different schemes to select which variables are accessible in the observation network, and measure impact on variable availability and length of captured execution trace.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".