W. Miao, Z. Xie, C. S. Tan and M. D. Rotaru, "Deep Reinforcement Learning-Based Power Distribution Network Design Optimization for Multi-Chiplet System," 2024 IEEE 74th Electronic Components and Technology Conference (ECTC), Denver, CO, USA, 2024, pp. 1716-1723, doi: 10.1109/ECTC51529.2024.00284.
Abstract:
This study employs a reinforcement learning (RL) technique, Dueling Double Deep Q Network (DDDQN), to optimize the power distribution network (PDN) in a high-density fan-out wafer-level package (HD-FOWLP) for a four-chiplet system, addressing the complexities of the PDN design with multiple power domains. A four-chiplet system is arranged in a ring topology, with each chiplet featuring seven distinct power islands, resulting in a total of 28 separate power islands within the power layer of the HD-FOWLP package's redistribution layer (RDL). The reinforcement learning algorithm used in this work addresses the difficulties of expansive design space and offers efficient optimization as well as shorter development time. A grid of 13 x 13 cells (in the format of a matrix) was used as the design environment to represent one quadrant of the package PDN; the design rules were translated into analytical expressions as the reward functions for RL. Training the RL model to generate a viable solution took 6 CPU hours. The proposed model demonstrates the ability to reduce development time and cost, easing engineers’ workload and development difficulty.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Applied Centre of Excellence in Advanced Packaging 3.0
Grant Reference no. : I2101E0008