Design and Optimization of Low-Dropout Voltage Regulator Using Relational Graph Neural Network and Reinforcement Learning in Open-Source SKY130 Process
Bibliographic record
Abstract
Design automation and optimization for analog integrated circuits (ICs) are challenging, especially for transistor sizing. Given certain design specifications and circuit topology, circuit designers need to size various components to achieve the desired performance, possibly involving many optimization iterations. Recently, reinforcement learning (RL) has been applied to optimize analog circuits. The trained RL agents can achieve very high sample efficiency over evolutionary-based algorithms. By using the ability of transfer learning, the trained agent can be applied to optimize the same circuit across different technology nodes and even the circuits with different topologies. However, a significant bottleneck in applying machine learning (ML) techniques to analog IC design is the non-disclosure agreement (NDA) of the process development kit (PDK), which makes reproducibility of the prior art a big challenge. This work presents an RL framework that leverages the open-source SKY130 PDK to address the limitation above. We apply a novel heterogeneous graph neural network (GNN) called relational graph convolutional network (RGCN) as the function approximator of RL to capture more topological information about a circuit. As a proof-of-concept, low-dropout voltage regulators (LDO) are optimized by our proposed RL circuit optimizer framework to show its feasibility, achieving promising results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".