Using Dynamic Signal-Tracing to Debug Compiler-Optimized HLS Circuits on FPGAs
Bibliographic record
Abstract
High-level synthesis (HLS) for FPGA designs has received considerable attention in recent years. To make this design methodology mainstream, improved debugging technologies are essential. Ideally, a user should be able to debug their design using the original source code, without detailed knowledge of the underlying hardware, while the circuit executes in-situ. Although recent work has made progress toward this goal, existing solutions are unable to provide visibility into circuits that have been heavily optimized by the compiler. HLS compilers typically perform many optimizations, including moving variable values out of memories and into registers distributed throughout the design. Debugging such circuits typically requires either understanding the hardware and probing the appropriate RTL level registers, or ignoring these variables while debugging the design, neither of which is desirable. In this work we present a new signal-tracing technique, specifically designed for circuits that have been optimized by an HLS tool. Information is extracted from the HLS process to determine which signals are relevant to record each cycle. We automatically embed circuitry which dynamically selects the relevant signals, cycle-by-cycle, and records them into on-chip memories. In addition, we explore techniques to balance tracing between cycles to further improve memory efficiency. For each 100Kb of memory allocated to trace buffers, our technique can, on average, record and replay 4322 lines of source code, versus 141 lines using traditional tracing methods.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".