Signal Integrity and Power Leakage Optimization for 3D X-Point Memory Operation using Reinforcement Learning

Kyungjune Son, Keunwoo Kim, Gapyeol Park, Daehwan Lho, Hyunwook Park, Boogyo Sim, Taein Shin, Joonsang Park, Haeyeon Kim, Joungho Kim, Kyubong Gong
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Abstract

As the signal integrity (SI) issues become critical with high bandwidth and density applications, the SI analysis and optimization are necessary. The SI optimization loop including design, modeling, simulation, analysis and revision is repetitive and confined to specific applications. To overcome the recurrent issues, we proposed reinforcement learning (RL) model for SI and power leakage optimization in 3D X-Point memory operation. We defined the MDP components to reflect the optimization problem and the RL model shows learning convergence. The optimal design shows 6.2 % of crosstalk, 17.7 % of IR drop and 25.3 % of power leakage improvement than original design.
基于强化学习的三维x点存储操作信号完整性和功率泄漏优化
随着信号完整性问题在高带宽和密度应用中变得越来越重要,信号完整性分析和优化是必要的。包括设计、建模、仿真、分析和修订在内的SI优化循环是重复的,并且仅限于特定的应用。为了克服反复出现的问题,我们提出了3D x点存储操作中SI和功率泄漏优化的强化学习(RL)模型。我们定义了MDP组件来反映优化问题,RL模型具有学习收敛性。与原设计相比,优化后的系统串扰降低6.2%,红外下降17.7%,漏功率降低25.3%。
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