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RRAM-VAC: A Variability-Aware Controller for RRAM-based Memory Architectures

机译:RRAM-VAC:用于基于RRAM的内存体系结构的可变性感知控制器

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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.
机译:对互联,智能和节能设备的需求不断增长,要求它们醒来时既要提供超低待机功率,又要提供相对较高的计算能力。在这种情况下,新兴的电阻式内存技术(RRAM)可以作为一种有前途的解决方案,因为它们能够实现与CMOS,快速切换和非易失性存储的廉价细粒度技术的集成。但是,RRAM技术存在一些基本缺陷,例如器件之间的强耦合性以及周期与周期之间的可变性,这些缺陷会因老化而恶化,从而迫使设计人员考虑最坏情况的设计条件。在这项工作中,我们首次提出了一种从能量和性能两方面都可以利用最近发布的写端接(WT)电路的电路。建议的RRAM可变性感知控制器(RRAM-VAC)在触发实际的写入过程之前,先存储并合并来自处理器的写入请求。通过这样做,它可以平均RRAM的可变性,并使系统能够以内存编程时间分配平均值而不是最坏情况下的末尾运行。我们探索了针对各种RRAM可变性规范的所提出解决方案的设计空间,用实际的应用程序存储器轨迹对所提出的存储器控​​制器的效果进行了基准测试,并展示了(对于所​​考虑的RRAM技术规范)性能提高了44%至50%,从10% 85%的能量获取取决于应用程序内存访问模式。

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