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Recruiting Decay for Dynamic Power Reduction in Set-Associative Caches

机译:集合衰变中的动态功耗降低的衰减

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In this paper, we propose a novel approach to reduce dynamic power in set-associative caches that leverages on a leakage-saving proposal, namely Cache Decay. We thus open the possibility to unify dynamic and leakage management in the same framework. The main intuition is that in a decaying cache, dead lines in a set need not be searched. Thus, rather than trying to predict which cache way holds a specific line, we predict, for each way, whether the line could be live in it. We access all the ways that possibly contain the live line and we call this way selection. In contrast to way-prediction, way-selection cannot be wrong: the line is either in the selected ways or not in the cache. The important implication is that we have a fixed hit time - indispensable for both performance and ease-of-implementation reasons. One would expect way-selection to be inferior to sophisticated way-prediction in terms of the total ways accessed, but in fact it can even do better. To achieve this level of accuracy we use Decaying Bloom filters to track only the live lines in ways - dead lines are automatically purged. We offer efficient implementations of such autonomously Decaying Bloom filters, using novel quasi-static cells. Our prediction approach affords us high-accuracy in narrowing the choice of ways for hits as well as the ability to predict misses - a known weakness of way-prediction - thus outperforming sophisticated way-prediction. Furthermore, our approach scales significantly better than way-prediction to higher associativity. We show that decay is a necessary component in this approach - way-selection and Bloom filters alone cannot compete with sophisticated way-prediction. We compare our approach to Multi-MRU and we show that without even considering leakage savings - we surpass it terms of relative power savings and in relative energy-delay in 4-way (9%) and more so in 8-way (20%) and 16-way caches (31%).
机译:在本文中,我们提出了一种新的方法来减少集关联缓存中的动态功耗,该方法利用了一种节省泄漏的建议,即“缓存衰减”。因此,我们打开了在同一框架中统一动态和泄漏管理的可能性。主要直觉是,在递减的缓存中,不需要搜索集合中的虚线。因此,我们没有尝试预测哪种高速缓存方式保存特定行,而是针对每种方式预测该行是否可以存在于其中。我们访问可能包含实时线路的所有方式,我们称这种方式选择。与行进预测相反,行进选择不会出错:该行要么处于选定的方式,要么不在缓存中。重要的含义是我们具有固定的命中时间-从性能和易于实现的角度来看都是必不可少的。人们可能希望在选择的方法总数方面,方法选择不如复杂的方法预测,但实际上它甚至可以做得更好。为了达到这种精确度,我们使用“衰减衰落”过滤器以某种方式仅跟踪活动线路-自动清除无效线路。我们使用新颖的准静态单元,提供了这种自主衰减衰落布鲁姆滤波器的有效实现。我们的预测方法为我们提供了精确的方法,可以缩小命中方法的选择范围以及预测未命中的能力-已知的预测方法的弱点-从而胜过复杂的预测方法。此外,对于更高的关联性,我们的方法的扩展性明显优于路途预测。我们证明了衰减是这种方法的必要组成部分-单独的选路和Bloom过滤器无法与复杂的方式预测竞争。我们将我们的方法与Multi-MRU进行了比较,结果表明,即使不考虑泄漏节省,我们在4方向(9%)方面的相对功耗和相对能量延迟都超过了它,在8方向(20%)方面则超过了)和16路缓存(占31%)。

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