Configuration Scheduling Using Temporal Locality and Kernel Correlation
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines runtime decisions to configure hardware accelerators or execute in software. Traditionally, reconfigurable FPGAs are reconfigured on-demand with the hardware accelerator, as it is needed by the application. If the software kernel which the hardware kernel replaces is available too, then more sophisticated decision making on reconfigurations may lead to improved execution time and reduced power consumption. The temporal locality algorithm is proposed for applications where individual kernels dominate during differing execution modes. The kernel correlation algorithm is proposed for applications where sequences of kernels are invoked in regular patterns. SystemC simulation is used to compare these two scheduling algorithms against the on-demand policy. Both timing and power consumption results are presented. They indicate that a fairly large reconfiguration time is required for configuration scheduling to be beneficial.
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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 | 0.000 | 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 | 0.001 | 0.002 |
| 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 it