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Variation-aware speed binning of multi-core processors

机译:多核处理器的变化感知速度合并

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Number of cores per multi-core processor die, as well as variation between the maximum operating frequency of individual cores, is rapidly increasing. This makes performance binning of multi-core processors a non-trivial task. In this paper, we study, for the first time, multi-core binning metrics and strategies to evaluate them efficiently. We discuss two multi-core binning metrics with high correlation to processor throughput for different types of workloads and different process variation scenarios. More importantly, we demonstrate the importance of leveraging variation model data in the binning process to significantly reduce the binning overhead with a negligible loss in binning quality. For example, we demonstrate that the performance binning overhead of a 64-core processor can be decreased by 51% and 36% using the proposed variation-aware core clustering and curve fitting strategies respectively. Experiments were performed using a manufacturing variation model based on real 65 nm silicon data.
机译:每个多核处理器芯片的内核数量以及各个内核的最大工作频率之间的差异正在迅速增加。这使得对多核处理器进行性能分级成为一项不平凡的任务。在本文中,我们首次研究了多核分级指标和策略以对其进行有效评估。对于不同类型的工作负载和不同的流程变化方案,我们讨论了两个与处理器吞吐量高度相关的多核分箱指标。更重要的是,我们证明了在装箱过程中利用变化模型数据来显着减少装箱开销且装箱质量损失可忽略不计的重要性。例如,我们证明,使用建议的变体感知核心聚类和曲线拟合策略,可以分别将64核处理器的性能分箱开销降低51%和36%。使用基于实际65 nm硅数据的制造差异模型进行实验。

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