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EVAL: Utilizing processors with variation-induced timing errors

机译:评估:利用具有变化诱导的时序错误的处理器

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Parameter variation in integrated circuits causes sections of a chip to be slower than others. If, to prevent any resulting timing errors, we design processors for worst-case parameter values, we may lose substantial performance. An alternate approach explored in this paper is to design for closer to nominal values, and provide some transistor budget to tolerate unavoidable variation-induced errors. To assess this approach, this paper first presents a novel framework that shows how microarchitecture techniques can trade off variation-induced errors for power and processor frequency. Then, the paper introduces an effective technique to maximize performance and minimize power in the presence of variation-induced errors, namely High-Dimensional dynamic adaptation. For efficiency, the technique is implemented using a machine-learning algorithm. The results show that our best configuration increases processor frequency by 56% on average, allowing the processor to cycle 21% faster than without variation. Processor performance increases by 40% on average, resulting in a performance that is 14% higher than without variation — at only a 10.6% area cost.
机译:集成电路的参数变化导致芯片的部分比其他芯片慢。如果要防止任何产生的定时错误,我们设计用于最坏情况参数值的处理器,我们可能会失去大量的性能。本文探索的替代方法是设计更接近标称值,并提供一些晶体管预算以容忍不可避免的变化引起的误差。为了评估这种方法,本文首先提出了一种新颖的框架,了解微架构技术如何如何衡往功率和处理器频率的变化引起的误差。然后,本文介绍了一种有效的技术,可以最大化性能并在存在变化引起的误差情况下最小化功率,即高维动态适应。为了效率,使用机器学习算法实现该技术。结果表明,我们的最佳配置平均增加了56%的处理器频率,允许处理器快速循环21%而不是变化。处理器性能平均增长40%,导致性能高于无差异的14% - 仅为10.6%的面积成本。

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