GO Game Inspired Algorithm for Hardware Software Partitioning in Multiprocessor Embedded Systems
Bibliographic record
Abstract
The codesign is a robust methodology, used in modern embedded systems with the objective of achieving the functional specifications and meeting the non-functional requirements. The most interesting step in the codesing is the process of Hardware/Software Partitioning. The aim is to decide which functionalities of the system should be implemented in hardware ($HW$) or in software ($SW$). In this article, a new heuristic algorithm is proposed to simultaneously optimize the hardware area (cost) and the execution time (performance) of a multiprocessor system. The proposed algorithm is inspired from game theory and especially from the GO game. The system is modeled using the DAG graph (Data Acyclic Graph), and two players (HW player and SW player) play in turn and choose a block (functionality) from the graph (system). The HW player has the goal of optimizing the global HW area while the SW player has the objective of minimizing the global execution time. After the game termination, and based on the 0-1 Knapsack algorithm, a step of refinement is used to meet the constraint on the total hardware area or on the overall execution time if a constraint is pre-defined. Experimental results show that the proposed algorithm gives better solutions compared to the Simulated Annealing algorithm and the Genetic Algorithm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".