F-LEMMA: Fast Learning-Based Energy Management for Multi-/Many-Core Processors

Abstract

Over the last two decades, as microprocessors have evolved to achieve higher computational performance, their power density has also increased at an accelerated rate. Improving energy efficiency and reducing power consumption are therefore critically important to modern computing systems. One effective technique for improving energy efficiency is dynamic voltage and frequency scaling (DVFS). With the emergence of integrated voltage regulators (IVRs), the speed of DVFS can reach microsecond ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> ) timescales. However, a practical and effective strategy to guide fast DVFS remains a challenge. In this article, we propose F-LEMMA: a fast, learning-based, hierarchical DVFS framework consisting of a global power allocator in the kernel space, a reinforcement learning-based power management scheme at the architecture level, and a swift controller at the digital circuit level. This hierarchical approach leverages computation at the system and architecture levels with the short response time of the swift controller to achieve effective and rapid <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> -level power management supported by the IVR. Our experimental results demonstrate that F-LEMMA can achieve significant energy savings (35.2%) across a broad range of workloads. Conservatively compared with existing state-of-the-art DVFS-based power management schemes that can only operate at millisecond timescales, F-LEMMA can provide notable (up to 11%) energy-delay product (EDP) improvements across benchmarks. Compared with state-of-the-art nonlearning-based power management, our method has a universally positive effect on evaluated benchmarks, proving its adaptability.