Bibliographic record
Abstract
Saving and restoring an FPGA task state in an orderly manner is essential to enable hardware checkpointing, which is highly desirable to improve the ability to debug cloud-scale hardware services, and context switching, which allows multiple users to share FPGA resources. However, these features require task interruption, and stopping a task at an arbitrary time can cause several hazards including deadlock and data loss. In this article, we build a context saving and restoring simulator to simulate and identify these hazards. In addition, we derive design rules that should be followed to achieve safe task interruption. Finally, we propose task wrappers that can be placed around an FPGA task to implement these rules. The timing and area overheads added by these wrappers are very small; they add 1.8% area and no timing overhead to a full Memcached system. Taken together, these design rules and wrappers enable safe checkpointing and context switching in a wide variety of FPGA tasks, including those with multiple clocks, multi-cycle I/O transactions, and interface dependencies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.094 | 0.040 |
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".