Reinforcement Learning Applied to the Optimization of Power Delivery Networks with Multiple Voltage Domains

被引：2

作者：

Han, Seunghyup ^{[1
]}

Bhatti, Osama Waqar ^{[1
]}

Na, Woo-Jin ^{[2
]}

Swaminathan, Madhavan ^{[1
]}

机构：

[1] Georgia Inst Technol, Atlanta, GA 30332 USA

[2] Samsung Elect, Memory Div, Suwon, South Korea

来源：

2023 IEEE MTT-S INTERNATIONAL CONFERENCE ON NUMERICAL ELECTROMAGNETIC AND MULTIPHYSICS MODELING AND OPTIMIZATION, NEMO | 2023年

关键词：

reinforcement learning (RL); proximal policy optimization (PPO); power plane; power delivery network;

D O I：

10.1109/NEMO56117.2023.10202224

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper proposes a reinforcement learning (RL)based method to optimize power plane design with multiple voltage domains. The proposed method enables the simultaneous selection of the plane for expansion and its expansion direction, determining the size and shape of multiple power planes. The results show that the proposed method provides various optimized power plane designs that satisfy the target impedance and ensure the reference plane for the input/output (I/O) interface.

引用

页码：147 / 150

页数：4

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