Software-based Dynamic Overlays Require Fast, Fine-grained Partial Reconfiguration
Bibliographic record
Abstract
In this paper, we consider dynamic overlays which use fine-grained partial reconfiguration (PR) to continuously adapt to their software-based workload. In particular, we show how to modify a traditional (static) overlay developed for OpenVX into a dynamic overlay. We use a Xilinx FPGA, and show that the dynamic overlay needs unsupported features including faster PR, relocatability, and fine-grained configuration is needed for performance. Since these features are not available in Xilinx FPGAs, we estimate the application-level speedup they would provide. We find that vector custom instruction (VCI) chaining, which allow a VCI to directly cascade its result into another VCI is also essential. Overall, we find the static overlay achieves a speedup of roughly 20x faster than a Cortex-A9 processor, but with improved PR and chaining a speedup of 106x is attainable. While there have been calls for fast, fine-grained PR devices for decades, we believe that dynamic overlays may be the first true "killer application" that will justify adding these features to all FPGA devices.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".