Scalable Transformer Network-based Reinforcement Learning Method for PSIJ Optimization in HBM
Author:
Affiliation:
1. School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST),Daejeon,South Korea
2. Solution Development Team (Memory Business), Samsung Electronics,Hwaseong,South Korea
3. SK Hynix,Icheon,South Korea
Funder
National Research Foundation of Korea
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9947062/9947092/09947166.pdf?arnumber=9947166
Reference9 articles.
1. Policy Gradient Reinforcement Learning-based Optimal Decoupling Capacitor Design Method for 2.5-D/3-D ICs using Transformer Network
2. Reinforcement Learning for the Optimization of Decoupling Capacitors in Power Delivery Networks
3. An Enhanced Deep Reinforcement Learning Algorithm for Decoupling Capacitor Selection in Power Distribution Network Design
4. A graph placement methodology for fast chip design
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Fast and Accurate Computation of Wireless Power Transfer System Optimal Design Using Particle Swarm Optimization Method;IEEE Transactions on Electromagnetic Compatibility;2023-12
2. Power Supply Induced Jitter (PSIJ) Modeling, Analysis, and Optimization of High Bandwidth Memory (HBM) I/O Interface;2023 IEEE Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMC+SIPI);2023-07-29
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