Abstract
This paper proposes a hybrid algorithm combining reinforcement learning (RL) and a genetic algorithm (GA) for PDN decap optimization. The trained RL agent uses a graph convolutional neural network as a policy network and predicts the decap solution for a given PDN impedance and target impedance, which is seeded as an initial population to the GA. The trained RL agent is scalable regarding the number of decap ports. The main goal is to save computation time and find the near global minimum or global minimum. Generalization of the algorithm to different decap libraries is achieved through transfer learning, eventually reducing the training time of the RL agent. The proposed algorithm finds a decap solution satisfying target impedance twice as fast compared with genetic algorithms.
Recommended Citation
H. Manoharan et al., "Graph Convolutional Neural Network Assisted Genetic Algorithm for PDN Decap Optimization," IEEE International Symposium on Electromagnetic Compatibility, pp. 146 - 150, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/EMCSIPI49824.2024.10705608
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
decap optimization; genetic algorithm; graph convolutional neural network; transfer learning
International Standard Serial Number (ISSN)
2158-1118; 1077-4076
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024
Comments
National Science Foundation, Grant IIP-1916535