Using Partial Reconfiguration and Message Passing to Enable FPGA-Based Generic Computing Platforms
Bibliographic record
Abstract
Partial reconfiguration (PR) is an FPGA feature that allows the modification of certain parts of an FPGA while the rest of the system continues to operate without disruption. This distinctive characteristic of FPGAs has many potential benefits but also challenges. The lack of good CAD tools and the deep hardware knowledge requirement result in a hard-to-use feature. In this paper, the new partition-based Xilinx PR flow is used to incorporate PR within our MPI-based message-passing framework to allow hardware designers to createtemplate bitstreams, which are predesigned, prerouted, generic bitstreams that can be reused for multiple applications. As an example of the generality of this approach, four different applications that use the same template bitstream are run consecutively, with a PR operation performed at the beginning of each application to instantiate the desired application engine. We demonstrate a simplified, reusable, high-level, and portable PR interface for X86-FPGA hybrid machines. PR issues such as local resets of reconfigurable modules and context saving and restoring are addressed in this paper followed by some examples and preliminary PR overhead measurements.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".