Signal-Tracing Techniques for In-System FPGA Debugging of High-Level Synthesis Circuits
Why this work is in the frame
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Bibliographic record
Abstract
High-level synthesis (HLS) promises to increase designer productivity in the face of increasing field-programmable gate array sizes, and broaden the market of use, allowing software designers to reap the benefits of hardware implementation. One roadblock to HLS adoption is the lack of an in-system debugging infrastructure. Although designers can run their software code on a workstation, or simulate the register-transfer level, neither can reliably capture the behaviors, and therefore bugs, that may be present in the final system. Debugging hardware circuits in-system requires using signal-tracing to record circuit behavior for later offline analysis. In this paper, we present a debugging architecture, which automatically records key hardware signals, and relates them back to the original software source code. This architecture allows designers to debug HLS circuits in-system, in the context of the original source code. We present several signal-tracing techniques, tailored to HLS circuits, which allow a much longer execution trace to be captured. These techniques include signal compression, dynamically changing which signals are recorded cycle-by-cycle, and offline signal restoration. Compared to using an embedded logic analyzer to perform signal-tracing, our architecture increases the length of execution trace that can be recorded by 127X. For each 100 Kb of trace buffer memory, our architecture can record 15 369 executed lines of C code.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it