Kyungjune Son, Keunwoo Kim, Gapyeol Park, Daehwan Lho, Hyunwook Park, Boogyo Sim, Taein Shin, Joonsang Park, Haeyeon Kim, Joungho Kim, Kyubong Gong
{"title":"Signal Integrity and Power Leakage Optimization for 3D X-Point Memory Operation using Reinforcement Learning","authors":"Kyungjune Son, Keunwoo Kim, Gapyeol Park, Daehwan Lho, Hyunwook Park, Boogyo Sim, Taein Shin, Joonsang Park, Haeyeon Kim, Joungho Kim, Kyubong Gong","doi":"10.1109/EPEPS53828.2022.9947197","DOIUrl":null,"url":null,"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.","PeriodicalId":284818,"journal":{"name":"2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EPEPS53828.2022.9947197","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.