Efficient On-line Hardware/Software Task Scheduling for Dynamic Run-time Reconfigurable Systems
Bibliographic record
Abstract
Modern reconfigurable devices such as FPGAs can be reconfigured at run time. Some of them can be dynamically partially reconfigured, which means part of the FPGA is changed without interrupting other parts. This feature adds tremendous flexibility to the Reconfigurable Computing (RC) Field but also introduces challenges. Reconfigurable Operating Systems tend to ease applications development and most importantly applications verifications and maintenance. In this paper we propose novel scheduling algorithms for reconfigurable computing that can handle both hardware and software tasks. The algorithms proposed reuse hardware tasks to reduce reconfiguration overhead, migrate tasks between software/hardware, and give priority to hardware tasks. Results obtained indicate that adding a software processor element not only adds flexibility, but also increases system performance. Two on-line schedulers were designed and implemented. RCSched-I is a simple based implementation that nominates the first available free Partial Reconfigurable Region (PRR) for new tasks. RCSched-II on the other hand nominates any free PRR. Both schedulers check the nominated PRR(s) against the ready task for a match, then decide if there is a need for reconfiguration or not. RCSched-II reconfigures the least recently configured PRR, which increases hardware tasks reuse and decreases total processing time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".