基于ncfeet处理器的能量优化

Sami Salamin, Martin Rapp, H. Amrouch, A. Gerstlauer, J. Henkel
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引用次数: 3

摘要

能耗是所有现代处理器的关键优化目标。负电容场效应晶体管(ncfet)是一种领先的新兴技术,除了具有更好的能源效率外,还承诺具有出色的性能。附加铁电层的厚度、频率和电压是NCFET技术中影响处理器功率和频率的关键参数。然而,它们对能源优化的共同影响尚未得到研究。在这项工作中,我们首次证明了传统的(即ncfet不知道的)动态电压/频率缩放(DVFS)技术在应用于基于ncfet的处理器时,将能量最小化是次优的。我们进一步证明,在能量方面,最先进的ncfet感知电压缩放功率最小化也是次优的。这项工作提供了第一个ncfet感知的DVFS技术,该技术通过最佳运行频率/电压选择来优化处理器的能量。在ncfet中,能量最优频率和电压取决于工作负载和技术参数。我们的ncfet感知DVFS技术考虑这些影响,根据工作负载特性在运行时执行最佳电压/频率选择。结果表明,与传统的DVFS技术相比,可节省高达90%的能源。与最先进的ncfet感知电源管理相比,我们的技术可节省高达72%的能源,同时性能提高3.7倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy Optimization in NCFET-based Processors
Energy consumption is a key optimization goal for all modern processors. Negative Capacitance Field-Effect Transistors (NCFETs) are a leading emerging technology that promises outstanding performance in addition to better energy efficiency. Thickness of the additional ferroelectric layer, frequency, and voltage are the key parameters in NCFET technology that impact the power and frequency of processors. However, their joint impact on energy optimization has not been investigated yet.In this work, we are the first to demonstrate that conventional (i.e., NCFET-unaware) dynamic voltage/frequency scaling (DVFS) techniques to minimize energy are sub-optimal when applied to NCFET-based processors. We further demonstrate that state-of-the-art NCFET-aware voltage scaling for power minimization is also sub-optimal when it comes to energy. This work provides the first NCFET-aware DVFS technique that optimizes the processor's energy through optimal runtime frequency/voltage selection. In NCFETs, energy-optimal frequency and voltage are dependent on the workload and technology parameters. Our NCFET-aware DVFS technique considers these effects to perform optimal voltage/frequency selection at runtime depending on workload characteristics. Results show up to 90 % energy savings compared to conventional DVFS techniques. Compared to state-of-the-art NCFET-aware power management, our technique provides up to 72 % energy savings along with 3.7x higher performance.
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