基于片上机器学习的多核片上系统的变化感知异质电压调节

Joseph Riad, Jianhao Chen, E. Sánchez-Sinencio, Peng Li
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引用次数: 1

摘要

大规模的片上系统(soc)具有严格的电源要求,以确保为片上设备提供足够的电源并防止灾难性的时序违规。异质电压调节(HVR)利用片内和片外电压调节器的组合,已被提倡以最大效率确保电源完整性。然而,在以前的HVR工作中,不可避免的过程和温度变化并没有被考虑。在本文中,我们对过程和温度变化对HVR的影响进行了深入的评估。此外,我们提出了一个系统性的解决方案,将变化意识纳入HVR系统控制策略,以最小的硬件开销进一步提高系统功率效率4.28%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Variation-Aware Heterogeneous Voltage Regulation for Multi-Core Systems-on-a-Chip with On-Chip Machine Learning
Large-scale systems-on-a-chips (SoCs) have stringent power requirements to ensure adequate supply of power to on-die devices and prevent catastrophic timing violations. Heterogeneous voltage regulation (HVR) leveraging a combination of on-chip and off-chip voltage regulators has been advocated for ensuring power integrity with maximum efficiency. However, unavoidable process and temperature variations have not been considered in prior HVR work. In this paper, we present an in-depth evaluation of the impacts of process and temperature variations on HVR. Furthermore, we propose a systemic solution to incorporate variation awareness into the HVR system control policy to add a further improvement of up to 4.28% in system power efficiency with minimal hardware overhead.
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