具有TSV自动校准方案和基于机器学习的布局优化的192gb 12-High 896 gb /s HBM3 DRAM

Myeong-Jae Park, H. Cho, T. Yun, S. Byeon, Young Jun Koo, Sang-Sic Yoon, Dong-Uk Lee, Seokwoo Choi, Ji Hwan Park, Jinhyung Lee, Kyungjun Cho, Junil Moon, B. Yoon, Y. Park, Sangmuk Oh, C. Lee, Tae-Kyun Kim, S. Lee, Hyunwoo Kim, Yucheon Ju, SeungGyeon Lim, S. Baek, Kyo Yun Lee, Sang Hun Lee, Woodward We, Seungchan Kim, Yongseok Choi, Seong-Hak Lee, Seungtaek Yang, Gunho Lee, In-Keun Kim, Y. Jeon, Jaewon Park, J. Yun, Chanhee Park, Sun-Yeol Kim, Sungjin Kim, Dong-Yeol Lee, Su-Hyun Oh, T. Hwang, Junghyun Shin, Yu-Ri Lee, Hyunsik Kim, Jaeseung Lee, Youngdo Hur, Sangkwon Lee, Jieun Jang, J. Chun, Joohwan Cho
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引用次数: 16

摘要

自从推出高带宽内存(HBM DRAM)及其后续产品线以来,HBM DRAM一直被誉为解决内存墙问题的杰出解决方案。为了适应不断增长的系统级需求,我们推出了采用多种新功能和设计方案的HBM3 DRAM。实现了诸如片上ECC引擎、内部NN-DFE I/O信令、TSV自动校准和基于机器学习算法的布局优化等技术,以有效控制时间倾斜余量和SI退化权衡。此外,降低电压波动允许提高内存带宽,密度,功率效率和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A 192-Gb 12-High 896-GB/s HBM3 DRAM with a TSV Auto-Calibration Scheme and Machine-Learning-Based Layout Optimization
Ever since the introduction of high bandwidth memory (HBM DRAM) and its succeeding line-ups, HBM DRAM has been heralded as a prominent solution to tackle the memory wall problem. However, despite continual memory advancements the advent of high-end systems, including supercomputers, hyper-scale data centers and machine learning accelerators, are expediting requirements for higher-performance memory solutions. To accommodate the increasing system-level demands, we introduce HBM3 DRAM, which employs multiple new features and design schemes. Techniques such as an on-die ECC engine, internal NN-DFE I/O signaling, TSV auto-calibration, and layout optimization based on machine-learning algorithms are implemented to efficiently control timing skew margins and SI degradation trade-offs. Furthermore, reduced voltage swings allow for improved memory bandwidth, density, power efficiency and reliability.
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