Integrated design space exploration based on power-performance trade-off using genetic algorithm
Bibliographic record
Abstract
This paper presents a novel approach for Design Space Exploration (DSE) of integrated scheduling, allocation and binding in High Level Synthesis based on user specified power consumption and execution time constraints using multi structure Genetic Algorithm (GA). A pioneering effort has been made to explore the power-performance tradeoffs related to design of VLSI applications. The new cost function comprising of execution time is useful for data pipelined applications since it considers latency, cycle time (resulting from initiation interval) and number of sets of pipelined data. The GA based DSE initiates with a novel seeding process for parents as proposed in this paper which guarantees that the final solution will be optimal/near optimal. The proposed approach when verified for number of benchmarks yielded superior results in terms of power optimization and latency compared to a recent GA based approach. Moreover, the efficiency of the proposed approach was demonstrated by the fact that the experimental results also indicated the optimized performance (or execution time for pipelined data) as well as the optimal clock frequency for implementation which the current approach was unable to find.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 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".