Integrated scheduling, allocation and binding in High Level Synthesis for performance-area tradeoff of digital media applications
Bibliographic record
Abstract
This paper presents a novel Genetic Algorithm based exploration approach for integrated scheduling, allocation and binding in High Level Synthesis for Digital Media applications. The contributions of the proposed approach in this paper are: (a) Exploration of performance-hardware area tradeoffs of DSP digital media applications (b) Novel multi structure topology for chromosome encoding (c) Introduction of a novel cost function based on data pipelined performance-hardware area constraints (d) Novel load factor heuristic for chromosome decoding (e) Novel seeding process of the initial population based on serial and parallel implementation logic (f) Novel merit score (M-score) technique to assess the efficiency of the proposed approach (g) Novel cost value (C-value) metric that assesses the quality of final integrated solution. The proposed approach obtained an improvement of ≈ 2 % in quality of final solution compared to a current technique as well as achieved an efficiency of 58.33 % compared to same current approach, when applied on the digital media applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".