Discrepancy in Execution Time: Static vs. Dynamic Reconfigurable Hardware
Bibliographic record
Abstract
Utilization of FPGAs has increased dramatically in various application domains, mainly due to their unique traits. Especially, FPGAs' dynamic partial reconfiguration feature enables multiple and complex/large applications to be executed on a single chip, regardless of them fitting on chip. From our previous work on dynamic partial reconfigurable hardware for complex applications, we observed a discrepancy in execution time between static reconfigurable hardware (SRH) versus dynamic reconfigurable hardware (DRH). In this paper, we perform additional experiments/analysis to investigate this discrepancy, using a simple adder and a multiplier to compose our SRH and DRH designs. Our experimental results and analysis demonstrate that this discrepancy is not due to actual add/multiply operations, but due to difference in read/write operations to/from SDRAM for SRH vs. DRH, and difference between the two hardware versions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".