Phase-Based Accurate Power Modeling for Mobile Application Processors
Total Page：16
File Type：pdf, Size：1020Kb
electronics Article Phase-Based Accurate Power Modeling for Mobile Application Processors Kitak Lee 1,2 , Seung-Ryeol Ohk 1, Seong-Geun Lim 1 and Young-Jin Kim 1,* 1 Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Korea; [email protected] (K.L.); [email protected] (S.-R.O.); [email protected] (S.-G.L.) 2 System LSI Business, Samsung Electronics Co., Ltd., Hwaseong 18448, Korea * Correspondence: [email protected] Abstract: Modern mobile application processors are required to execute heavier workloads while the battery capacity is rarely increased. This trend leads to the need for a power model that can analyze the power consumed by CPU and GPU at run-time, which are the key components of the application processor in terms of power savings. We propose novel CPU and GPU power models based on the phases using performance monitoring counters for smartphones. Our phase-based power models employ combined per-phase power modeling methods to achieve more accurate power consumption estimations, unlike existing power models. The proposed CPU power model shows estimation errors of 2.51% for ARM Cortex A-53 and 1.97% for Samsung M1 on average, and the proposed GPU power model shows an average error of 8.92% for the Mali-T880. In addition, we integrate proposed CPU and GPU models with the latest display power model into a holistic power model. Our holistic power model can estimate the smartphone0s total power consumption with an error of 6.36% on average while running nine 3D game benchmarks, improving the error rate by about 56% compared with the latest prior model. Citation: Lee, K.; Ohk, S.-R.; Lim, Keywords: power model; power estimation; application processors; phase classification; smartphones S.-G.; Kim, Y.-J. Phase-Based Accurate Power Modeling for Mobile Application Processors. Electronics 2021, 10, 1197. https://doi.org/ 1. Introduction 10.3390/electronics10101197 According to YouGov, a UK international market research firm, the most respondents Academic Editor: Akash Kumar answered “Longer battery life” to the question “Which one of the following features would you most like your current smartphone to have?” [1]. Therefore, power management on a Received: 14 March 2021 smartphone is very important, and we need to carefully analyze the power consumed by Accepted: 11 May 2021 each component of the smartphone. Published: 17 May 2021 Figure1 shows the power breakdowns of a Galaxy S3 smartphone [ 2] and a Galaxy S7 smartphone, respectively. Figure1b shows the result estimated by the power models Publisher’s Note: MDPI stays neutral proposed in this paper while an application processor executes twenty 3D game benchmark with regard to jurisdictional claims in applications. As shown in Figure1, since the power consumption ratio of CPU+GPU over published maps and institutional affil- the total system power has increased from 57% to 85%, it is very important to analyze CPU iations. and GPU power accurately for power management on smartphones. This is because bigger power consumption may give rise to more power estimation error. This trend makes low-power techniques more important such as dynamic voltage frequency scaling (DVFS). DVFS is a technique that adjusts frequency and voltage to Copyright: © 2021 by the authors. achieve the optimal trade-off point between power and performance. Many DVFS policies, Licensee MDPI, Basel, Switzerland. which are implemented in software at the Linux kernel level, employ information like This article is an open access article utilization to know the workload of a processor. If DVFS software can get information distributed under the terms and about power consumption of processors, there will more chances for DVFS policies to conditions of the Creative Commons make bigger energy efficiency. However, it is very difficult to estimate accurately the power Attribution (CC BY) license (https:// consumption of each processor at run-time without a built-in hardware sensor. creativecommons.org/licenses/by/ 4.0/). Electronics 2021, 10, 1197. https://doi.org/10.3390/electronics10101197 https://www.mdpi.com/journal/electronics ElectronicsElectronics 20212021, 10,, 119710, 1197 2 of 19 2 of 19 CPU GPU Display Base CPU GPU Display Base 4% 11% 25% 29% 40% 18% 56% 17% (a) (b) FigureFigure 1. 1.SmartphoneSmartphone power power breakdowns breakdowns while while running running game applications onon ((aa)) GalaxyGalaxy S3, S3, (b) Galaxy(b) Galaxy S7. S7. In this paper, we propose new phase-based power models that can estimate the power In this paper, we propose new phase-based power models that can estimate the consumption of CPU and GPU accurately, which are the core components of a mobile powerapplication consumption processor of (AP), CPU using and theGPU performance accurately monitoring, which are counter the core (PMC) components at run-time. of a mo- bileSince application our proposed processor power (AP), models using can the provide performance the power monitoring consumption counter information (PMC) of at run- time.each Since component our proposed every time powe interval,r models the power can provide management the power drivers consumption can implement information more ofpower-efficient each component management every time policies interval, using th suche power information. management In addition, drivers a can program implement moreprofiler power-efficient can provide software management engineers policies with powerusing consumptionsuch information. information In addition, of their a pro- gramprograms. profiler For can example, provide it couldsoftware be embeddedengineers inwith a system power analyzer consumption program information such as of theirARM programs. DS5-streamline For example, [3] to show it could developers be embedded the run-time in a system power consumptionanalyzer program of each such as ARMcomponent DS5-streamline while applications [3] to show are running. developers the run-time power consumption of each In detail, we propose a novel power modeling method which combines phase clas- componentsification and while PMC-based applications power are modeling. running. In addition, we integrate the proposed CPU andIn GPU detail, power wemodels propose with a novel an existing power state-of-the-art modeling method display which power combines model to derive phase a classi- ficationholistic and power PMC-based model that power can estimate modeling. the total Inpower addition, consumption we integrate of a smartphone. the proposed In CPU andextensive GPU power experiments, models we with show an how existing accurately state- ourof-the-art holistic power display model power can model estimate to the derive a holisticsystem power0s power model consumption that can while estimate executing the varioustotal power 3D game consumption benchmarks of on a a smartphone. Galaxy S7 In extensivesmartphone experiments, with an Android we show 7.1 platform. how accura The contributionstely our holistic of our power work are model summarized can estimate theas system follows.′s power consumption while executing various 3D game benchmarks on a Gal- axy• S7The smartphone proposed with power an model Android achieves 7.1 platform. the accuracy The improvementcontributions over of our the work existing are sum- marizedpower as follows. models by using multiple PMC-based per-phase power models and proposed phase classification and phase prediction methods for both CPU and GPU. • The proposed power model achieves the accuracy improvement over the existing • For the optimal GPU power model, optimal PMC events for linear regression predic- powertors are models searched by by using the geneticmultiple algorithm PMC-base [4]. d per-phase power models and proposed • phaseA holistic classification power model and isphase completed, prediction which methods estimates for the both smartphone CPU and total GPU. power • Forconsumption the optimal accurately, GPU power including model, the optimal power consumption PMC events of CPU,for linear GPU, regression display, and predic- torscomponents are searched connected by the to genetic them. algorithm [4]. • • AAll holistic the power power models model have is beencompleted, proven towhich be practical estimates because the allsmartphone implementations total power consumptionand verifications accurately, are conducted including on recent the smartphonespower consumption with an Android of CPU, platform. GPU, display, andThe components remainder of connected this paper isto organizedthem. as follows. Section2 introduces previous • studiesAll the about power CPU andmodels GPU have power been models. proven Section to3 be describes practical the because motivation all ofimplementations our work. Then,and Section verifications4 describes are an conducted overview of on our recent CPU andsmartphones GPU power with models, an Android and Sections platform.5 and6 explain our novel CPU and GPU power modeling approaches in detail. Sections7 andThe8 explain remainder the experimental of this paper environment is organized and experimental as follows. results, Section respectively. 2 introduces Finally, previous studiesSection about9 concludes CPU thisand paper. GPU power models. Section 3 describes the motivation of our work. TheThen, terminologies Section 4 describes used in the an remaining overview parts of ofour this CPU paper and are GPU described power as follows.models, and SectionsFirst, an 5 eventand 6 type explain means our the novel type ofCPU available and GPU events power that a modeling PMC can count.approaches The PMC in