Reproduce “Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning” on OpenResearch
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About this paper
Weidong Cao, Mouhacine Benosman, Xuan Zhang, Rui Ma
This paper presents a deep reinforcement learning framework that integrates domain knowledge into analog circuit design automation. The proposed GNN-FC policy network, combining Graph Neural Networks for topology and Fully Connected Neural Networks for specification relationships, achieves human-level design accuracy and improved efficiency on Op-Amp and RF PA circuits.
Key insights
- Incorporating domain knowledge, specifically circuit topology via GNNs and specification relationships via FCNNs, into DRL significantly enhances analog circuit design automation.
- The GAT-FC policy network, utilizing attention mechanisms, consistently outperforms GCN-FC and baseline DRL approaches, achieving higher efficiency and accuracy.
- A transfer learning strategy, pre-training with faster simulations and fine-tuning with accurate ones, effectively addresses the high computational cost of analog circuit simulations.
Summary by alphaXiv.
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