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

التفاصيل البيبلوغرافية
العنوان: Deep Reinforcement Learning-based Interconnection Design for 3D X-Point Array Structure Considering Signal Integrity
المؤلفون: Kyungjune Son, Taein Shin, Shinyoung Park, Seoungguk Kim, Daewhan Lho, Keeyoung Son, Minsu Kim, Keunwoo Kim, Gapyeol Park, Joungho Kim, HyunWook Park
المصدر: 2020 IEEE Electrical Design of Advanced Packaging and Systems (EDAPS).
بيانات النشر: IEEE, 2020.
سنة النشر: 2020
مصطلحات موضوعية: Interconnection, Computer science, Convergence (routing), Reinforcement learning, Array data structure, Signal integrity, Solid modeling, Markov decision process, Power network design, Algorithm
الوصف: 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.
DOI: 10.1109/edaps50281.2020.9312891
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::bb5f03f993823ab704f5b3b37ca69488
https://doi.org/10.1109/edaps50281.2020.9312891
Rights: CLOSED
رقم الانضمام: edsair.doi...........bb5f03f993823ab704f5b3b37ca69488
قاعدة البيانات: OpenAIRE
الوصف
DOI:10.1109/edaps50281.2020.9312891