An FPGA Overlay Architecture Supporting Rapid Implementation of Functional Changes during On-Chip Debug
Bibliographic record
Abstract
As Field-Programmable Gate Arrays become more complex, debugging designs implemented on these devices has become increasingly time-consuming. For many types of bugs, simulation is not sufficient, and the only way to uncover the root cause of unexpected behaviour is to run the design in hardware at speed. Many techniques that support on-chip debug have been described; typically, these techniques involve instrumenting the design to increase observability. In this paper, we describe instrumentation that not only increases observability, but that can also be used to control certain aspects of the design. Supported functional changes include applying small deviations in the control flow of the circuit, or the ability to override signal assignments to perform efficient "what if'" tests. Our approach uses a novel overlay architecture which allows these changes to be implemented during debug without recompiling the design. Changes can be made in seconds, dramatically reducing the time to perform a debug iteration. Our overlay is specifically optimized for designs created using a high-level synthesis (HLS) flow; by taking advantage of information from the HLS tool, the overhead of the overlay can be kept low.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".