Presentation
POG: Parameter Optimization using Graph Neural Networks on Reinforcement Learning
DescriptionThis paper proposes POG, a novel surrogate model for analog circuit parameter optimization using reinforcement learning (RL). POG embeds circuit features using separate voltage and current graph
neural networks (GNNs) that consider different topologies and directed edges, reflecting the physical behavior of circuits more accurately. These embeddings are used in policy and value networks to determine circuit parameters. Our experiments with realistic circuits demonstrate that POG achieves faster convergence and improved parameter optimization compared to other configurations.
neural networks (GNNs) that consider different topologies and directed edges, reflecting the physical behavior of circuits more accurately. These embeddings are used in policy and value networks to determine circuit parameters. Our experiments with realistic circuits demonstrate that POG achieves faster convergence and improved parameter optimization compared to other configurations.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby
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