A multi-objective decision-theoretic exploration algorithm for platform-based 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 paper presents an efficient technique to perform multi-objective design space exploration of a multiprocessor platform. Instead of using semi-random search algorithms (like simulated annealing, tabu search, genetic algorithms, etc.), we use the domain knowledge derived from the platform architecture to set-up the exploration as a discrete-space multi-objective Markov Decision Process (MDP). The system walks the design space changing its parameters, performing simulations only when probabilistic information becomes insufficient for a decision. The algorithm employs a novel multi-objective value function and exploration strategy, which guarantees high accuracy and minimizes the number of necessary simulations. The proposed technique has been tested with a small benchmark (to compare the results against exhaustive exploration) and two large applications (to prove effectiveness in a real case), namely the ffmpeg transcoder and pigz parallel compressor. Results show that the exploration can be performed with 10% of the simulations necessary for state-of-the-art exploration algorithms and with unrivaled accuracy (0.6 ± 0.05% error).
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 | 0.000 | 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 | 0.002 |
| 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