Facilitating Processor-Based DPR Systems for non-DPR Experts
Bibliographic record
Abstract
Currently, only Xilinx field programmable gate arrays (FPGAs) support dynamic partial reconfiguration (DPR). While there is currently some computer aided design (CAD) tool support for ISE-based DPR designs, none exists for microprocessor-based designs created in EDK. Creating DPR systems with the limited tool support currently available for ISE-based systems is already a challenging and complex process for novice DPR designers. These difficulties are severely compounded for potential microprocessor-based designs requiring a significant learning curve for novice DPR designers before they can successfully create their first working DPR system. This paper presents preliminary work towards extending the automation in Xilinx®'s current DPR design flow to include microprocessor based systems. The objective is to abstract low level details for novice designers, allowing them to focus on learning how to improve the quality of their design as opposed to how to perform the necessary manual transformations to generate a preliminary functional design. A case study demonstrated that the learning curve required to implement a first working design could be reduced by more than a factor of 15 times by improving the current automation available for microprocessor-based EDK designs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".