HypAp: A Hypervolume-Based Approach for Refining the Design of Embedded Systems
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
Designing complex embedded systems requires simultaneous optimization of multiple system performance metrics that can be addressed by applying Pareto-based multiobjective optimization techniques. At the end of this type of optimization process, designers always face Pareto fronts (PFs) including a large number of near-optimal solutions from which selecting the most proper system implementation is potentially infeasible. In this letter, for the first time, we present HypAp, a hypervolume-based automated approach to systematically help designers efficiently choose their preferred solutions after the optimization process. HypAp is a two-stage approach relying on clustering Pareto optimal solutions and then finding a subset of solutions that maximizes the hypervolume by using a genetic algorithm. The performance of HypAp is evaluated through applying HypAp to the PF by the case study of mapping applications on network-on-chip-based heterogeneous MPSoC.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.007 | 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