Modeling and Evaluation of Dynamic Partial Reconfigurable Datapaths for FPGA-Based Systems Using Stochastic Networks
Bibliographic record
Abstract
The dynamic partial reconfiguration of FPGAs is a method which modifies parts of FPGA configuration memory at run-time. The hardware resources and time overhead needed to perform a partial reconfiguration (PR) can significantly impact overall system cost and performance and must be considered early in the design cycle. Unfortunately, predicting reconfiguration overhead is difficult especially in the presence of non-deterministic factors such as the sharing of resources with traffic not related to the PR process. Thus, current design practices include the measurement of overhead but only after the system has been built thus limiting the number of candidates that can be evaluated. We propose a flexible approach for modeling the PR datapath based on Queueing Theory such that we can estimate performance trends and bottlenecks of the PR process while considering the impact of shared resources. Performance trends are provided for an example system to demonstrate the effectiveness of the approach.
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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 | 0.003 | 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 | 0.000 | 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".