PSIJ Based Optimal PDN Design for Cost-Effective SSD Using Reinforcement Learning
Taein Shin, Seonguk Choi, Jungmin Ahn, Junghyun Lee, Keunwoo Kim, Haeseok Suh, Hyunah Park, Haeyeon Kim, Hyunjun An, Jinwook Song, Joungho Kim · 2024
In this paper, we first propose power supply noise induced jitter (PSIJ) based optimal power distribution network (PDN) design using reinforcement learning (RL) to achieve cost-effective solid state drive (SSD). Compared to the traditional PDN impedance design, designing based on PSIJ, which includes current noise and timing information, allows for more precise PDN optimization. The proposed method allows for the development of an optimal PDN design that meets the target PSIJ with minimal decoupling capacitor (decap) area usage. In order to achieve a cost-effective SSD board design, the RL reward includes both PSIJ and the decap area. This enables the RL agent to learn to satisfy the target PSIJ using minimal resources from decap library. To derive PSIJ, the process includes adding decaps to the z-parameters of the SSD board's PDN plane to extract PDN impedance and modeling PSIJ in the frequency domain through jitter sensitivity. Finally, the successful optimization is demonstrated through performance comparison with a representative genetic algorithm (GA).