Efficient Mapping and Allocation of Execution Units to Task Graphs using an Evolutionary Framework
Why this work is in the frame
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Bibliographic record
Abstract
Partial dynamic reconfiguration of FPGAs gives designers the capability to change certain parts of the hardware while other parts remain active and in use. This provides several benefits including reducing device count and power consumption. However, this also introduces new challenges that need to be addressed by designers. This paper introduces a framework for efficient mapping of execution units to task graphs in a runtime reconfigurable system. The framework utilizes an Island Based Genetic Algorithm flow that optimizes several objectives including delay and power consumption. The GA based technique not only optimizes the above objectives, but also aggregates the Pareto front of the different islands to further enhance solution quality. The Island based GA runs each GA in parallel, and is amenable to both software and hardware implementation. The proposed Island based GA framework achieves on average 55.2% improvement over a single GA implementation and 80.7% improvement over a baseline random allocation and binding approach.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it