基于强化学习的输电网电源平面优化设计

2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS) Pub Date : 2022-10-09 DOI:10.1109/EPEPS53828.2022.9947173

Seunghyup Han, O. W. Bhatti, Madhavan Swaminathan

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引用次数: 5

摘要

提出了一种基于深度确定性策略梯度(DDPG)的输电网络电源平面优化方法。该方法考虑平面设计在一层中的自由度，确定创建动力平面的参数。结果表明，该方法可以在具有受限区域的平面层中提供优化的功率平面设计。

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Reinforcement Learning for the Optimization of Power Plane Designs in Power Delivery Networks

This paper proposes a deep deterministic policy gradient (DDPG) based method to optimize the power plane in power delivery networks (PDNs). The proposed method considers the degrees of freedom of a plane design in a layer, determining the parameters for creating a power plane. The results show that the proposed method can provide an optimized power plane design even in a plane layer with a restricted region.

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来源期刊

2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)

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