A comparative evaluation of multi-objective exploration algorithms for high-level design
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 article presents a detailed overview and the experimental comparison of 15 multi-objective design-space exploration (DSE) algorithms for high-level design. These algorithms are collected from recent literature and include heuristic, evolutionary, and statistical methods. To provide a fair comparison, the algorithms are classified according to the approach used and examined against a large set of metrics. In particular, the effectiveness of each algorithm was evaluated for the optimization of a multiprocessor platform, considering initial setup effort, rate of convergence, scalability, and quality of the resulting optimization. Our experiments are performed with statistical rigor, using a set of very diverse benchmark applications (a video converter, a parallel compression algorithm, and a fast Fourier transformation algorithm) to take a large spectrum of realistic workloads into account. Our results provide insights on the effort required to apply each algorithm to a target design space, the number of simulations it requires, its accuracy, and its precision. These insights are used to draw guidelines for the choice of DSE algorithms according to the type and size of design space to be optimized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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