RRAM-VAC:基于rram的存储器结构的可变感知控制器

Shikhar Tuli, M. Rios, A. Levisse, David Atienza Alonso
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引用次数: 7

摘要

对联网、智能和节能设备日益增长的需求要求它们在唤醒时提供超低待机功率和相对较高的计算能力。在这种情况下,新兴的电阻式存储技术(RRAM)成为一种很有前途的解决方案,因为它们使廉价的细粒度技术与CMOS、快速开关和非易失性存储协同集成。然而,RRAM技术存在一些根本性的缺陷,比如器件间和周期间的可变性,这种可变性会因老化而恶化,这迫使设计师考虑最坏的设计情况。在这项工作中,我们首次从能量和性能的角度提出了一种可以利用最近发表的写终止(WT)电路的电路。所提出的RRAM可变性感知控制器(RRAM- vac)在触发实际写过程之前存储并合并来自处理器的写请求。通过这样做,它平均了RRAM可变性,并使系统能够在内存编程时间分布平均值上运行,而不是在最坏情况下运行。我们探索了针对各种RRAM可变性规范提出的解决方案的设计空间,用真实的应用程序内存跟踪对提出的内存控制器的效果进行基准测试,并根据应用程序内存访问模式显示(对于考虑的RRAM技术规范)44%至50%的性能改进和10%至85%的能量增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RRAM-VAC: A Variability-Aware Controller for RRAM-based Memory Architectures
The growing need for connected, smart and energy efficient devices requires them to provide both ultra-low standby power and relatively high computing capabilities when awoken. In this context, emerging resistive memory technologies (RRAM) appear as a promising solution as they enable cheap fine grain technology co-integration with CMOS, fast switching and non-volatile storage. However, RRAM technologies suffer from fundamental flaws such as a strong device-to-device and cycle-to-cycle variability which is worsened by aging, forcing the designers to consider worst case design conditions. In this work, we propose, for the first time, a circuit that can take advantage of recently published Write Termination (WT) circuits from both the energy and performances point of view. The proposed RRAM Variability Aware Controller (RRAM-VAC) stores and then coalesces the write requests from the processor before triggering the actual write process. By doing so, it averages the RRAM variability and enables the system to run at the memory programming time distribution mean rather than the worst case tail. We explore the design space of the proposed solution for various RRAM variability specifications, benchmark the effect of the proposed memory controller with real application memory traces and show (for the considered RRAM technology specifications) 44 % to 50 % performances improvement and from 10% to 85% energy gains depending on the application memory access patterns.
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