带关闭的块重映射:一种容错缓存设计技术

M. A. Hussain, M. Mutyam
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引用次数: 13

摘要

随着特征尺寸的减小,工艺变化的影响变得越来越突出。诸如片上缓存之类的存储器组件更容易受到这种变化的影响,因为它们具有高密度和小尺寸的晶体管。工艺变化会导致高访问延迟和泄漏能量耗散。这可能导致功能正确的芯片被拒绝,从而导致芯片成品率降低。在本文中,通过考虑影响片上数据缓存的工艺变化,我们首先分析了由于最坏情况设计技术(如使用最坏情况访问延迟访问整个缓存或关闭受最坏情况影响的缓存块)导致的性能损失,并表明最坏情况设计技术会导致显着的性能损失和/或高泄漏能量。然后,利用并非所有应用都需要集合级的完全结合性这一事实,我们提出了一种容差设计技术,即带关断的块重映射(BRT),以最大限度地减少性能损失和泄漏能量消耗。在BRT技术中,我们在重新排列后选择性地关闭一些块,使所有集获得几乎相同数量的过程变化影响块。通过关闭一个集合中受进程变化影响的块,可以最小化泄漏能量,以降低集合的结合性为代价,以低延迟访问该集合。我们通过在Simplescalar模拟器上运行SPEC2000 CPU基准套件验证了我们的技术,并表明我们的技术显着降低了由于工艺变化而导致的性能损失和泄漏能耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Block remap with turnoff: A variation-tolerant cache design technique
With reducing feature size, the effects of process variations are becoming more and more predominant. Memory components such as on-chip caches are more susceptible to such variations because of high density and small sized transistors present in them. Process variations can result in high access latency and leakage energy dissipation. This may lead to a functionally correct chip being rejected, resulting in reduced chip yield. In this paper, by considering a process variation affected on-chip data cache, we first analyze performance loss due to worst-case design techniques such as accessing the entire cache with the worst-case access latency or turning off the process variation affected cache blocks, and show that the worst-case design techniques result in significant performance loss and/or high leakage energy. Then by exploiting the fact that not all applications require full associativity at set-level, we propose a variation-tolerant design technique, namely, block remap with turnoff (BRT), to minimize performance loss and leakage energy consumption. In BRT technique we selectively turnoff few blocks after rearranging them in such a way that all sets get almost equal number of process variation affected blocks. By turning off process variation affected blocks of a set, leakage energy can be minimized and the set can be accessed with low latency at the cost of reduced set associativity. We validate our technique by running SPEC2000 CPU benchmark-suite on Simplescalar simulator and show that our technique significantly reduces the performance loss and leakage energy consumption due to process variations.
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