Integrated scheduling, allocation and binding in High Level Synthesis using multi structure genetic algorithm based design space exploration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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