Multi-Agent Reinforcement Learning in Designing the Low-Dropout Regulator Circuits
Bibliographic record
Abstract
Low-dropout regulator (LDO) circuit, whose function is to provide a power supply robust to variations in process, voltage, and temperature (PVT), is an essential part in any system-on-chip. Therefore, the design of this circuit must satisfy various intricate specifications, rendering the design process generally perceived as tedious and lengthy. As a result, previous research has been conducted to explore the use of machine learning, particularly reinforcement learning, in speeding up and automating the LDO design process, especially the sizing phase. However, the results of these works are limited in terms of the number of design variables and specifications handled by the automation engine. This study presents the application of single-agent proximal policy optimization (PPO) and multi-agent proximal policy optimization (MAPPO), including both parameter-separated and parameter-sharing methods, to address the LDO sizing automation problem. The experimental result shows that the PPO- and MAPPO-based implementation in LDO sizing automation outperforms that of the classical particle swarm optimization algorithm. We demonstrate that the parameter-separated MAPPO features the most effective learning process compared with other PPO-based benchmarks, resulting in a design result that is competitive to that of a well-known commercial tool.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".