Bibliographic record
Abstract
Debugging consumes a large portion of FPGA design time, and with the growing complexity of traditional FPGA systems and the additional verification challenges posed by multiple FPGAs interacting within data centers, debugging productivity is becoming even more important. Current debugging flows either depend on simulation, which is extremely slow but has full visibility, or on hardware execution, which is fast but provides very limited control and visibility. In this paper, we present StateMover, a checkpointing-based debugging framework for FPGAs, which can move design state back and forth between an FPGA and a simulator in a seamless way. StateMover leverages the speed of hardware execution and the full visibility and ease-of-use of a simulator. This enables a novel debugging flow that has a software-like combination of speed with full observability and controllability. StateMover adds minimal hardware to the design to safely stop the design under test so that its state can be extracted or modified in an orderly manner. The added hardware has no timing overhead and a very small area overhead. StateMover currently supports Xilinx UltraScale devices, and its underlying techniques and tools can be ported to other device families that support configuration readback. Moving the state from/to an FPGA to/from a simulator can be performed in a few seconds for large FPGAs, enabling a new debugging flow.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.021 |
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".