Communication-model based embedded mapping of dataflow actors on heterogeneous MPSoC
Bibliographic record
Abstract
Mapping a dataflow application onto a heterogeneous multiprocessor platform cannot longer be static. It has to adapt dynamically depending on the data and on the communication between the computation cores. This is typically the case for mobile devices that run multimedia applications. This paper presents an algorithm fast enough to be executed at run-time. In addition to computation cost, our approach relies on a communication model to estimate the delay for transmitting data. The algorithm is compared with METIS tool for random dataflow graphs and two video decoders, MPEG4-SP and HEVC, considering heterogeneous multiprocessor platforms composed of 4 to 8 processors and 6 accelerators. Results on a Zynq platform show that our algorithm is about 40x faster than METIS tool for the same throughput (frames per second) on a platform with 8 processors and 6 accelerators.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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 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".