Deep Reinforcement Learning-based Interconnection Design for 3D X-Point Array Structure Considering Signal Integrity

Abstract

In this paper, we, for the first time, proposed the Reinforcement Learning (RL) based interconnection design for 3D X-Point array structure considering crosstalk and IR drop. We applied the Markov Decision Process (MDP) to correspond to finding the optimal interconnection design problem to RL problem. We defined interconnection state to the vector, design to the action and the number of bits, crosstalk and IR drop are considered as the reward. The Proximal Policy Optimization (PPO) and Long Short-Term Memory (LSTM) are used to RL algorithms. The proposed interconnection design model is well trained and shows convergence of reward score in 16×16, 32×32 and 64×64 cases. We verified that the trained model finds out optimal interconnection design considering both memory size and signal integrity issues.