Integrated scheduling, allocation and binding in High Level Synthesis using multi structure genetic algorithm based design space exploration
Bibliographic record
Abstract
This paper presents a novel multi structure genetic algorithm based design space exploration system which concurrently solves the problem of integrated scheduling, allocation and binding in High Level Synthesis based on the user specified power consumption and execution time constraints (not just latency constraint). The proposed novel cost function based on power consumption and execution time considers functional units, registers, multiplexers, demultiplexers and clock frequency oscillator during the exploration process. The presented approach incorporates a new seeding process for the two special parent chromosomes as well as employs a novel `load factor heuristic' which guarantees that the final solution found will always be optimal/near-optimal in terms of the user specified execution time and power constraints. The results of the final solution reflect the number of adders/subtractors, multipliers, clock frequency, multiplexers, demultiplexers and registers. Further, the final result also indicates the latency, execution time, power consumption and the optimal/near-optimal resource combination found. The proposed approach when verified for number of standard DSP benchmarks yielded superior results compared to a recent GA based heuristic approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".