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Ultra-Dense Ring-Shaped Racetrack Memory Cache Design

机译:超密环形赛马场内存缓存设计

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摘要

Information storage and transfer via current-induced domain wall (DW) motions exhibit significant density-speed-energy advantages, which inspires numerous emerging devices and circuits, such as racetrack memory (RM). However, the bi-directional propagation of DWs in the conventional tape-shaped nanowire will lead to data overflow issue, implicitly deteriorating storage density and operational performances. In this paper, we propose a non-volatile cache design based on spin-orbit torque-driven ring-shaped RM. The systematical investigations, covering from device modeling, to circuits design, to bit-cell layout design, and to system evaluation have been carried out. Thanks to the cells-overlapping design, the proposed RM L2 cache can achieve$48imes $,$16imes $, and$8imes $improvements in term of capacity, compared with iso-area caches based on static random access memory (SRAM), spin transfer torque magnetic RAM (STT-MRAM), and tape-shaped RM, respectively. As proved by 4-core system experiment results, the proposed RM cache can improve 30.7% instructions per cycle (IPC) and save 58.2% energy compared with SRAM cache.
机译:通过电流感应畴壁(DW)运动进行的信息存储和传输显示出显着的密度-速度-能量优势,这激发了许多新兴的设备和电路,例如赛道存储器(RM)。然而,传统带状纳米线中DW的双向传播将导致数据溢出问题,从而暗中降低存储密度和操作性能。在本文中,我们提出了一种基于自旋轨道转矩驱动的环形RM的非易失性缓存设计。已经进行了系统的研究,涵盖了从器件建模,电路设计,位单元布局设计到系统评估的整个过程。多亏了单元重叠设计,建议的RM L2缓存可以实现 n $ 48 times $ n, n < tex-math符号= “ LaTeX ”> $ 16 次$ n和 n $ 8 times $ 相比基于静态随机存取存储器(SRAM),自旋传递转矩磁性RAM(STT-MRAM)和带状的等面积缓存的容量方面的改进RM,分别。正如四核系统实验结果所证明的,与SRAM缓存相比,所建议的RM缓存可以提高每个周期30.7%的指令(IPC)并节省58.2%的能量。

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  • 作者单位

    Fert Beijing Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), School of Microelectronics, Beihang University, Beijing, China;

    Fert Beijing Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), School of Microelectronics, Beihang University, Beijing, China;

    Fert Beijing Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), School of Microelectronics, Beihang University, Beijing, China;

    Fert Beijing Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), School of Microelectronics, Beihang University, Beijing, China;

    Fert Beijing Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), School of Microelectronics, Beihang University, Beijing, China;

    Fert Beijing Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), School of Microelectronics, Beihang University, Beijing, China;

    Fert Beijing Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), School of Microelectronics, Beihang University, Beijing, China;

    C2N, University of Paris-Sud, University of Paris-Saclay and UMR8622 CNRS, Orsay, France;

    C2N, University of Paris-Sud, University of Paris-Saclay and UMR8622 CNRS, Orsay, France;

    Fert Beijing Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), School of Microelectronics, Beihang University, Beijing, China;

    Fert Beijing Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), School of Microelectronics, Beihang University, Beijing, China;

    Fert Beijing Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), School of Microelectronics, Beihang University, Beijing, China;

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  • 正文语种 eng
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  • 关键词

    Random access memory; Magnetic tunneling; Performance evaluation; Magnetization; Frequency modulation; Magnetic domains; Energy consumption;

    机译:随机存取存储器磁性隧穿性能评估磁化频率调制磁畴能耗;

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