TY - GEN
T1 - Reinforcement Learning for the Optimization of Power Plane Designs in Power Delivery Networks
AU - Han, Seunghyup
AU - Bhatti, Osama Waqar
AU - Swaminathan, Madhavan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85143433102&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143433102&partnerID=8YFLogxK
U2 - 10.1109/EPEPS53828.2022.9947173
DO - 10.1109/EPEPS53828.2022.9947173
M3 - Conference contribution
AN - SCOPUS:85143433102
T3 - EPEPS 2022 - IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems
BT - EPEPS 2022 - IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st IEEE Conference on Electrical Performance of Electronic Packaging and Systems, EPEPS 2022
Y2 - 9 October 2022 through 12 October 2022
ER -