Research Article An Adaptive and Integrated Low-Power Framework for Multicore Mobile Computing

Transcription

1 Hindawi Mobile Informaion Sysems Volume 2017, Aricle ID , 11 pages hps://doi.org/ /2017/ Research Aricle An Adapive and Inegraed Low-Power Framework for Mulicore Mobile Compuing Jongmoo Choi, 1 Bumjong Jung, 1 Yongjae Choi, 1 and Seiil Son 2 1 Deparmen of Sofware, Dankook Universiy, Yongin, Republic of Korea 2 Korea Communicaions Agency, Daejeon, Republic of Korea Correspondence should be addressed o Jongmoo Choi; choijm@dankook.ac.kr Received 20 January 2017; Acceped 15 March 2017; Published 12 June 2017 Academic Edior: Karl Andersson Copyrigh 2017 Jongmoo Choi e al. This is an open access aricle disribued under he Creaive Commons Aribuion License, which permis unresriced use, disribuion, and reproducion in any medium, provided he original work is properly cied. Employing mulicore in mobile compuing such as smarphone and IoT (Inerne of Things) device is a double-edged sword. I provides ample compuing capabiliies required in recen inelligen mobile services including voice recogniion, image processing, big daa analysis, and deep learning. However, i requires a grea deal of power consumpion, which causes creaing a hermal ho spo and puing pressure on he energy resource in a mobile device. In his paper, we propose a novel framework ha inegraes wo well-known low-power echniques, DPM (Dynamic Power Managemen) and DVFS (Dynamic Volage and Frequency Scaling) for energy efficiency in mulicore mobile sysems. The key feaure of he proposed framework is adapabiliy. By monioring he online resource usage such as CPU uilizaion and power consumpion, he framework can orchesrae diverse DPM and DVFS policies according o workload characerisics. Real implemenaion based experimens using hree mobile devices have shown ha i can reduce he power consumpion ranging from 22% o 79%, while affecing negligibly he performance of workloads. 1. Inroducion Inelligen services are acively inroduced ino mobile compuing environmens [1, 2]. For insance, modern black box devices suppor no only he radiional recording service bu alsoadas(advanceddriverassisancesysems)suchas blind spo monioring, pedesrian deecion, and auomaic emergency braking. Smarphones provide voice recogniion and image processing for beer HCI (Human Compuer Inerface) and big daa processing and deep learning for conex-aware services wih he consideraion of user personaliy. To suppor such inelligen services efficienly, mobile compuing devices are equipped wih muliple cores. Smarphones have he heerogeneous mulicore archiecure, called big.little, which consiss of performance-opimized big cores and energy-opimized lile cores wih a single ISA (Insrucion Se Archiecure) [3]. Recenly released embedded boards for IoT (Inerne of Things) such as Raspberry Pi, Odroid, Edison, Jeson, and Arik also provide muliple cores [4 6]. However, employing mulicore in mobile devices riggers anewissue.ashenumberofcoresincreases,hepower consumpion used by cores becomes a significan porion in mobile devices. The increased power consumpion pus pressure on he energy resource in a mobile device due o he limiaion in baery capaciy [7]. In addiion, i causes he high inernal emperaure in a mobile device [8], having a poenial o resul in he hermal runaway [9]. To address he mulicore power consumpion issue, wo well-known echniques are devised [7, 10]. One is DPM (Dynamic Power Managemen), also known as offlining, which urns off individual cores in order o reduce he percore power consumpion [11]. The oher echnique is DVFS (Dynamic Volage and Frequency Scaling), which decreases hefrequencyandvolageofacore.[12]. One ineresing observaion is ha mos mobile applicaions have limied parallelism [13 15]. For insance, Seo e al. analyzehowhemuliplecoresareuilizedinmobiledevices using TLP (Thread Level Parallelism) and observe ha TLP of mos mobile applicaions is ranging from 1.4 o 3.9 [15]. I implies ha he required acive cores for he applicaions are

2 2 Mobile Informaion Sysems less han 3.9 on average, disclosing he opporuniy of DPM and DVFS. In his paper, we propose a new low-power framework for mulicore mobile devices. I suppors boh DPM and DVFS in an inegraed manner. Also, i keeps rack of he CPU load of applicaions and urns on/off cores or changes frequency adapively so ha i can reduce he power consumpion while rying o minimize is impac on performance. The framework consiss of hree componens, namely, online resource usage monior, lower-power conroller, and policy manager. The online resource monior gahers resource usage saisics such as CPU uilizaion and power measuremen. The lower-power conroller maerializes he DPM and DVFS echniques using he Linux CPU hoplug and governor mechanism, respecively [16, 17]. Finally, he policy manager provides a rule regarding how o inegrae DPM and DVFS echniques based on heir overhead and effec on he power consumpion. Also, i applies he inegraed soluion appropriaely according o he resource usage characerisics of applicaions. In addiion, i suppors user inerfaces so ha a user can configure his/her own conrol parameers. We have implemened he framework in he Linux kernel version 3.10 and have evaluaed is effeciveness using hree mobile devices. Experimenal resuls show ha he workloadaware adapabiliy of he framework can reduce he power consumpion ranging from 22% o 79%, while no affecing heperformanceofworkloadsgrealy.also,weobserveha he power reducion depends on he feaures of a mobile device, especially in he big.little archiecure. The res of his paper is organized as follows. In Secion 2, we explain previous sudies relaed o his work. Then, our proposed framework is discussed in Secion 3. Secion 4 presens real implemenaion based experimenal resuls. Finally, we summarize conclusion and fuure work in Secion Relaed Work As he energy efficiency becomes a criical issue, a lo of sudies have been conduced for analyzing and enhancing he power consumpion in mobile compuing sysems. In his secion, we classify hese sudies ino he following four caegories: power consumpion analysis, workload analysis, DPM/DVFS echniques, and sysem/applicaion-level approach Power Consumpion Analysis. Undersanding of where andhowpowerisconsumedinmobiledevicesisakey requiremen for efficien power managemen. Carroll and Heiser presen a deailed analysis of power consumpion in mobile phones using Google Nexus One, HTC Dream, and OpenMoko Neo Freerunner [18]. They provide no only he overall sysem power bu also he breakdown of power consumed by he main hardware componens. Their analysis shows ha wireless communicaion and display are he heavies power consumers. Also, CPU is idenified as a heavy consumer especially for he CPU inensive workloads andinhesuspendedsae. As he number of cores increases, he power consumpion used by cores also increases sharply. Several sudies demonsrae ha he power consumpion increases wih he number of acive cores and as he volage and frequency increase [6 8]. Tawara e al. also presen how he power consumpion affecs he inernal emperaure using hermography [8]. Zhu and Shen observe ha even hough he power consumpion increases wih he number of cores, he firs acivaed core incurs much higher power cos han each addiional core does in he same processor due o he shared resources [6] Workload Analysis. Even hough employing mulicore in mobile devices increases he power consumpion, i also gives an opporuniy o reduce he applicaion execuion ime, evenually leading o energy saving. To explore he opporuniy, several sudies have examined how much parallelism is hereinmobileapplicaions[13 15]. Gaoeal.analyzehowhemuliplecoresareuilized in mobile devices using TLP (Thread Level Parallelism) [13]. They repor ha TLP of he mos mobile applicaions including browser, map, music, and games is less han 2 on average, meaning ha he number of acive cores used by he majoriy of applicaions is below 2. Similar behavior is also observed by Zhang e al. [14]. Seo e al. invesigae TLP using various mobile benchmark applicaions in mobile devices and observe ha applicaions have TLP ranging from 1.4 o 3.9 [15]. These sudies uncover he necessiy o urn off cores or o adjus volage and frequency adapively according o differen program execuion phases DPM/DVFS Techniques. DPM and DVFS are wo wellknown echniques for dynamic energy-aware CPU managemens. For DPM, Linux provides he CPU hoplug mechanism ha allows cores o be added o or removed from a running kernel [16]. For DVFS, Linux suppors he governor mechanismhapermisuseroadjushecpufrequency [17]. There are several governors such as ondemand, powersave, and performance, which will be discussed furher in Secion 3. Carroll and Heiser explore how o use DPM and DVFS o reduce power consumpion [7, 19]. They propose a framework, called medusa, an offline-aware frequency governor, which inegraes core offlining wih frequency scaling. In addiion, hey find ha modern smarphones have quie differen characerisics, implying ha policies ha work well on one processor can lead o poor resuls on anoher. Tawara e al. design a framework, called idle reducion, whichurns on/off cores or changes he CPU frequency dynamically according o he inensiy of workloads [8]. These wo works are closely relaed o our work in ha hey inegrae DPM and DVFS and apply hem adapively basedonworkloadcharacerisics.however,ourproposed framework differs from hem in he following hree aspecs: (1) we consider no only he homogeneous bu also heerogeneous mulicore devices, (2) we carefully separae policy and mechanism o suppor flexibiliy, and (3) we provide a configuraion file wih various conrol parameers so ha a user can easily configure his/her preferred policy.

3 Mobile Informaion Sysems 3 Online resource usage monior Policy manager Framework Low-power conroller CPU hoplug Governor Odroid XU3 Raspberry Pi 2 Pine 64 Mobile compuing devices Figure 1: Srucure of he proposed low-power framework. Zhu e al. observe ha he wakeup delay of he sleeping cores akes hundreds of microseconds, which is a leas 100 imes slower han he frequency change delay [20]. To hide his laency, hey devise an anicipaory CPU wakeup for mainaining high performance. Song e al. propose a framework ha applies low-power echniques based on userperceived response ime analysis [21]. Wamhoff e al. design a library ha assigns heerogeneous and even boosed frequencies for acceleraing performance [22]. Chiesi e al. presen a power-aware scheduling algorihm on heerogeneous archiecures o reduce he peak power [23]. DPM and DVFS are also sudied in he real-ime research area [10, 24, 25]. Bambagini e al. presen a survey paper ha discusses various energy-aware scheduling algorihms for real-ime mobile sysems [24]. Li and Broekaer design a DVFS based low-power scheduler ha explois slack imes wih an inraask inrusive approach [25]. Chen e al. devise a echnique ha models he idle inervals of individual cores o opimize he DVFS and DPM for real-ime asks [10] Sysem/Applicaion-Level Approach. Roy e al. propose a new operaing sysem, called cinder, for mobile phones and energy-consrained compuing devices [26]. I suppors wo new absracions, reserves and aps, which sore and disribue energy for applicaions. Snowdon e al. design a framework; heyreferoiaskoala,whichprovidesamodel,ageneralized energy-delay policy, and a single parameer for uning he sysem o an overall energy-managemen objecive [27]. Shen e al. presen a new operaing sysem faciliy, called power conainers, ha conrols he power and energy usage of individual fine-grained requess in mulicore sysems [28]. Zhu and Shen observe he energy disproporionaliy in mulicore devices, where he firs running CPU incurs much higher power cos han each addiional core does [6]. Kwon e al. propose a framework ha predics he compuaional resource consumpion on mobile devices using program analysis and machine learning [29]. Thiagarajan e al. design an infrasrucure for measuring energy used by a mobile browser o render web pages such as , e-commerce, and social neworking sies [30]. Using he infrasrucure, hey observe ha downloading and parsing CCS (Cascade Syle Shees) and JavaScrip consume a large porion of energy and recommend how o design web pages for enhancing energy efficiency. Bui e al. propose echniques, namely, adapive conen paining and applicaionassised scheduling, o improve he energy efficiency of web page loading [31]. 3. Adapive and Inegraed Low-Power Framework In his secion, we firs explain he overall srucure of our framework and principle used o design he framework. Then, we discuss he deails of each componen in sequence Overall Srucure. Figure 1 displays he overall srucure of he adapive and inegraed low-power framework proposed in his paper. I consiss of hree componens, called online resource usage monior, policy manager, and lowpower conroller. When we design our framework, we use he design principle ha separaes policy and mechanism o suppor diverse policies flexibly. The policy manager akes charge of he policy decision, while he low-power conroller provides mechanisms for DPM and DVFS. The online resource monior is used for supporing adapabiliy based on resource usage paerns including he CPU uilizaion and power consumpion. Currenly, he framework works on hree mulicorebased mobile compuing devices, namely, Odroid XU3 [32], Raspberry Pi2 [33], and Pine 64 [34]. Noe ha he framework can be insalled any Linux-based devices since i uilizes he sandard Linux inerfaces only Low-Power Conroller. Thelow-powerconrollercomponen provides mechanisms for DPM and DVFS. Specifically, i makes use of he CPU hoplug mechanism [16] for DPM and hegovernormechanism[17]fordvfsasshowninfigure1. The CPU hoplug is a mechanism ha urns on and off an individual core dynamically. I was originally designed o

4 4 Mobile Informaion Sysems User space /sys/devices/sysem/cpu/cpu_number/online /sys/devices/sysem/cpu/cpu_number/cpufreq/scaling_governor Online Presen Acive Possible Performance Powersave Ondemand Conservaive Kernel space CPU hoplug Governor cpufreq module CPU-specific drivers Hardware Muliple cores Figure 2: Inernals of he low-power conroller. allow a failing core o be removed from a running sysem, bu now i is popularly used for energy managemen. Figure 2 shows he inernal behavior of he low-power conroller. The CPU hoplug mechanism suppors a file, /sys/devices/sysem/cpu/cpu_number/online file, ino user space using he sysfs file sysem ha is a pseudo file sysem exporing various kernel informaion in Linux. A user can urn on or off a core by wriing 1 or 0 o he file. While urning on or off a core, he core goes hrough various saes such as possible, presen, acive, and online [35].Aeachsae,hemechanisminvokesvariousfuncions provided by CPU-specific drivers such as cpu_up(), cpu_ online(), cpu_down(), and cpu_down_prepare(). Finally, he on/off reques is applied ino he designaed core a hardware level. The mos ime consuming job in he CPU hoplug mechanism is he conex managemen. To urn off a core, i needs o save he conex of a process ha runs on he core and migraes he conex o anoher core o coninue is execuion. Also,ournonacore,ihasoprepareanewscheduling queue for he core o be acive. Hence, he laency for a core on/off is much higher han ha for changing frequency of a core. We need o consider his difference for our inegraed low-power managemen. For DVFS, he low-power conroller makes use of he governor mechanism. I allows changing he frequency of a core dynamically. I suppors a file, /sys/devices/sysem/ cpu/cpu_number/cpufreq/scaling_governor, ino user space as shown in Figure 2. Linux employs several defaul governors, namely, performance, powersave, ondemand, conservaive, and userspace [17]. The disincions of hese governors are illusraed in Figure 3. In he performance governor, a core always runs a he maximum frequency. On he conrary, in he powersave governor, a core runs a he minimum frequency. In he ondemand governor, a core runs iniially a he minimum frequency. When a CPU load increases, he frequency becomes he maximum frequency immediaely o minimize he effec of DVFS on he applicaion execuion ime. When he load decreases, he frequency goes down gradually ino he minimum frequency. In he conservaive governor, a core runs iniially a he minimum frequency like he ondemand governor. Bu, when he load becomes inensive, he frequency increases gradually. Finally, he userspace governor allows any frequency o be se by a user. Commercial smarphones exend hese governors, devising heir own specific governors. For insance, Android uses a governor, called ineracive governor, which behaves similar o he ondemand governor bu responds more quickly [15]. Each governor is based on a subsysem, called cpufreq, which provides inerfaces o explicily se frequency on cores. This subsysem evenually makes use of he CPU-specific drivers ha acually implemen CPU on/off and frequency change funcionaliy for various vendors including ARM, Inel, and AMD. The low-power conroller inegraes he CPU hoplug and governor mechanisms and provides virual inerfaces such as increase_compuing() and decrease_compuing(). These inerfaces invoke he mechanisms appropriaely based on he decision dicaed by he policy manager, which will be furher discussed in Secion Online Resource Usage Monior. The online resource usage monior gives informaion abou how much resources are used by he curren workload so ha our framework can apply he low-power mechanisms adapively. I makes use of he Linux proc file sysem, measuring various resource usage saisics such as CPU uilizaion, memory fooprin, power consumpion, and process aciviies. In addiion, i repors he measured saisics via a web page using Node.js. Figure 4 presens he CPU uilizaion saisics measured by he monior on he Odroid XU3 device. This device has he big.little archiecure, consising of four lile cores (ARM Corex-A7) and four big cores (ARM Corex-A15). The figure shows ha he firs lile core (CPU0) runs a he frequency of 1.4 GHz whose uilizaion for user, sysem, and idle sae is 6.79%, 3.61%, and 88.72%, respecively. To measure he power consumpion, he monior makes use of he power measuremen funcionaliy

5 Mobile Informaion Sysems 5 Max freq Work load Max freq Work load Min freq Min freq T o T o (a) Performance (b) Powersave Work load Work load Max freq Min freq Max freq Min freq T o T o (c) Ondemand (d) Conservaive Figure 3: Frequency changes in various governors. Home CPU Memory Power Process ETC CPU Performance Odroid XU3 board: 4 lile cores (A7) and 4 big cores (A15) A7 A15 CPU STAT CPU USAGE CPU MHz 6.79% 3.61% 88.72% CPU MHz 4.76% 2.15% 92.07% CPU MHz 4.70% 1.99% 92.17% CPU MHz 4.45% 1.80% 92.74% CPU MHz 67 C 2.76% 2.66% 93.81% CPU MHz 58 C 2.15% 2.42% 94.61% CPU MHz 67 C 2.71% 2.79% 93.88% CPU MHz 67 C 1.88% 2.14% 95.12% Figure 4: CPU uilizaion repored by he online resource usage monior on he Odroid XU3 device. already equipped in a mobile device. The Odroid XU3 device suppors such funcionaliy, providing he power consumpion of each uni including big core, lile core, GPU, and RAM. For devices ha do no suppor such funcionaliy, Raspberry Pi 2 and Pine 64 in his paper, we uilize an exernal power meer ha can quanify he overall power consumpion by assessing he volage driven ino he power uni Policy Manager. Based on he measured informaion providedbyheonlineresourceusagemonior,hepolicy manager makes a policy ha can lead o beer energy efficiency. Then, i enforces he policy using inerfaces suppored by he low-power conroller. The policy manager inroduces four conrol parameers, namely, max_uilizaion, min_uilizaion, max_frequency, and min_frequency. The former wo parameers are used o rigger he policy manager o enforce is policy while he laer wo parameers are used for DVFS. For insance, when he curren uilizaion of a core is higher han he max_uilizaion, he policy manager is riggered o increase compuing resource. Since our framework inegraes wo echniques, DPM and DVFS, we need o have a rule abou which echnique is applied firs. The policy manager suppors wo opions. The firs opion is providing an inerface so ha a user can specify his/her preference. The second opion is giving a defaul rule by considering he overhead of he wo echniques and feaures of mobile devices. To devise a guideline for he defaul rule, we analyze heoverheadofheechniquesandhepowerreducedby hem using he online resource usage monior. We make he

6 6 Mobile Informaion Sysems decrease_compuing() for each big core do if (core.freq > parameer.big.min_freq) hen decrease core.freq o he nex lower level for each big core do if (core.sae == on) hen urn off his core for each lile core do if (core.freq > parameer.lile.min_freq) hen decrease core.freq o he nex lower level for each lile core do if (core.sae == on) hen urn off his core increase_compuing() for each lile core do if (core.sae == off) hen urn on his core for each lile core do if (core.freq < parameer.lile.max_freq) hen increase core.freq o he nex higher level for each big core do if (core.sae == off) hen urn on his core for each big core do if (core.freq < parameer.big.max_freq) hen increase core.freq o he nex higher level Algorihm 1: Pseudocode for decreasing compuing resources in he policy manager. Algorihm 2: Pseudocode for increasing compuing resources in he policy manager. following hree observaions. Firs, he laency o urn off a core is much longer han ha o change he frequency of a core. Second, he power saved by urning off a core is smaller han ha by decreasing he frequency of a core. This resul is also observed by Carroll and Heiser s sudy where hey recommend ha one should offline cores conservaively and reduce frequency aggressively [19]. This is parly because cores share various resources in he same processor [6]. Third, he power used by big cores is much higher han ha by lile cores. Our observaions lead us o design an algorihm, shown in Algorihm 1, which is invoked when we decrease compuing resource. The algorihm prefers big cores o lile ones and prefers DVFS o DPM by defaul. Specifically, i firs ries o reduce he frequency among big cores. If i is no possible, i ries o urn off a big core. Again, if no feasible, i ries o reduce he frequency and o urn off a core among lile cores. When one of he four if condiions saisfies, his funcion s wihou going furher. Algorihm 2 presens an algorihm for increasing compuing resources. The sequence of his algorihm is reverse o ha of he one in Algorihm 1. I gives a higher prioriy for lile cores. Then, i ries o urn on a core before increasing he frequency. The nex higher level and he nex lower level in he pseudocodes are deermined by he governors, discussed in Figure 3. The policy manager provides a configuraion file so ha a user can configure his/her preferred parameers. The parameers include min/max uilizaion, min/max frequency, DPM/DVFS preference, monioring period, and monioring number. The defaul values for he preference, monioring period, and monioring number are DPM preference, 200 milliseconds, and 5, respecively. The monioring number is he number of consecuive measuremens for calculaing he moving average of he CPU uilizaion. When i is small, he policy ries o apply he low-power echniques Device Odroid XU3 [32] Raspberry Pi 2 [33] Pine 64 [34] Table 1: Mulicore-based mobile devices. Mulicore descripion Oca cores (Corex-A15 4cores,Corex-A74 cores) Quad cores (Corex-A7 4cores) Quad cores (Corex-A53 4cores) aggressively. When a user prefers DVFS, he firs/hird par of he pseudocodes is changed wih he second/fourh par. 4. Evaluaion In his secion, we firs describe our experimenal devices and workloads considered in his paper. Then, we discuss evaluaion resuls from he power measuremens o he power and energy saving achieved by he proposed framework Experimenal Environmens. We have implemened he framework on he Linux kernel version The online resource usage monior uses he proc file sysem for monioring and repors usage saisics via a web page using Node.js. The low-power manager provides he inegraed lowpower inerfaces based on he CPU hoplug and governor mechanism. The policy manager is implemened as a daemon process ha analyzes resource usage saisics a every 200 milliseconds and applies he inegraed low-power inerfaces when he curren CPU uilizaion is above/below he hreshold of he max/min uilizaion, whose defaul values are 80% and 20% in his experimen. Table1summarizeshreemobilecompuingdevicesused in his sudy. The Odroid XU3 device has he big.little

7 Mobile Informaion Sysems 7 (a) Video recording (b) Pedesrian deecion archiecure, consising of four lile cores (Corex-A7) and four big cores (Corex-A15). Each core suppors he same ISA and equips L1, L2 cache, where he size of L2 cache for lile and big core is 512 KB and 2 MB, respecively. Boh he Raspberry Pi 2 and Pine 64 devices have homogeneous four cores, Corex-A7 and Corex-A53, respecively. Noe ha he mulicore archiecure used in Odroid XU3, also called Exynos 5422, is acually employed for commercial mobile devices including Samsung Galaxy S5 and Chromebook 2. We use hree workloads for experimens. The firs one is a video recording, as shown in Figure 5(a). I is an I/O inensive workload, recording using a camera and displaying hrough LCD. The second one is he Sunspider es suie [36]. I is a CPU inensive workload ha ess JavaScrip performance including funcion calls, mah, and recursion wihou rendering. The hird one is a pedesrian deecion using he Haar classifier [37]. (Wa) Figure 5: Workloads for experimens Lile core Big core GPU DRAM Figure 6: Power measuremen resuls under he idle sae on he Odroid XU3 device (Wa) 4.2. Evaluaion Resuls Video Recording Workload Resuls. This secion consiss of wo pars. In he firs par, we explain evaluaion resuls observed using he Odroid XU3 device ha has heerogeneous 8 cores. The second par is he resuls of he Raspberry Pi2 and Pine 64 device ha have homogeneous 4 cores. Noe ha, in Odroid XU3, we can measure he power consumpion of each componen individually using he measuremen funcionaliy already equipped in he device while, in Raspberry Pi2 and Pine 64, we only measure he overall power consumed by he device using he exernal power meer, as discussed in Secion 3.3. Figure 6 presens he power measuremen resuls of he Odroid XU3 device when i is in an idle sae. The resuls are repored by he online resource usage monior discussed in Secion 3.3. This measuremen is conduced under he baseline configuraion where all hardware componens are powered on. The resuls reveal ha big cores are he heavies power consumer, using wa, while lile cores, GPU, and DRAM consume 0.155, 0.055, and wa, respecively. Noe ha big cores consume quie large power even in Lile core 0.13 Big core GPU DRAM Baseline AI-framework Figure 7: Power consumpion comparison under he video recording workload on he Odroid XU 3 device. an idle sae, demonsraing he imporance of he DPM and DVFS echniques. To evaluae he power consumpion reduced by our proposed framework, we execue he video recording workload under he wo configuraions, as shown in Figure 7. The firs configuraion is he baseline where any low-power

8 8 Mobile Informaion Sysems (Wa) Idle Recording Idle Recording (a) Raspberry Pi 2 (b) Pine 64 Baseline AI-framework Figure 8: Power consumpion comparison on he Raspberry Pi 2 and Pine 64 device. Number of acive cores Times (second) Figure 9: The number of acive cores when we execue he Sunspider workload on he Odroid XU3 device. echnique is no applied. The second configuraion is under our framework, labelled as AI-framework (Adapive and Inegraed framework) in he figure. Since he video recording is no a CPU inensive workload, i uses only one core during mos of is execuion period. Hence, our framework urns on one lile core, while urning off oher cores. On he conrary, in he baseline, all cores are powered on and he workload mosly runs on one of he big cores. As a resul, he power consumed by big cores becomes 2.44 wa in he baseline, while ha consumed in he AIframework is 0.13 wa due o offlining. The power consumed by lile cores is 0.42 wa in he AI-framework, higher han ha in he baseline. The overall power consumed by all cores in he baseline is 2.66 wa while ha consumed in he AIframework is 0.55 wa (79% reducion). Figure 8 presens he power measuremen resuls using RaspberryPi2andPine64underheidleandhevideo recording case. Noe ha hese devices provide he frequency change funcionaliy only, no supporing he dynamic power off funcionaliy for an individual core. Hence, in his experimen, he AI-framework explois he DVFS echnique only. The resuls show ha our proposed AI-framework can reduce he power consumpion by decreasing CPU frequency appropriaely. For Raspberry Pi2, i reduces he power consumpion from 0.27 o 0.23 wa under he idle sae and from 0.47 o 0.36 wa when we run he video recording workload. For Pine 64, i reduces from 0.34 o 0.24 wa and from 0.75 o 0.73 wa, respecively. The reducion is relaively lowforhevideorecordingworkloadonhepine64device. We conjecure ha he camera module equipped in he Pine 64 device consumes a large porion of power consumpion, leading o his small difference. We leave he componen-level fine-grainedpoweranalysisashefuurework Sunspider Workload Resuls. Figure 9 presens he number of acive cores ha are powered on during he execuion period of he Sunspider workload in he AIframework on he Odroid XU3 device. I shows ha when he workload requires a large compuing resource, he number of acive cores increases up o he maximum cores. When Relaive comparison Power Execuion ime Energy Baseline AI-framework 0.81 Figure 10: Power consumpion, execuion ime, and energy saving comparison using he Sunspider workload on he Odroid XU 3 device. he workload does no need ha much compuing resource, he acive cores decrease down o he one core. I reveals ha our framework indeed suppors adapabiliy according o he workload characerisics. Figure 10 presens he power consumpion, execuion ime, and consumed energy when we run he Sunspider workloadunderhebaselineandai-framework.noeha he y-axis is he relaive value. The power consumed in he baseline is 2.6 wa while ha in he AI-framework is 1.6 wa (37% reducion). However, he execuion ime of he workload in he baseline is 39 seconds while ha in he AI-framework is 49 seconds (27% degradaion). I shows he radeoff of he low-power echniques, reducing power consumpion a he expense of performance drop. As a ne resul, he AIframework can achieve he 19% energy saving, reducing from 1.97 o 1.6 joule. For a CPU inensive workload, we can miigae he performance drop by urning off cores conservaively, which will be furher discussed in he nex secion. We also run he Sunspider workload on he Pine 64 and Raspberry Pi 2 device. The resuls show ha even hough he

9 Mobile Informaion Sysems 9 Table 2: Power consumpion and deecion laency on he Pine 64 and Raspberry Pi 2 device. Table 3: Power consumpion and deecion laency on he Odroid XU3 device (max uilizaion: 80%, min uilizaion: various). Device Pine 64 Raspberry Pi2 Configuraion Power (wa) Laency (ms) AI-framework Baseline 1: max freq Baseline 2: min freq AI-framework Baseline 1: max freq Baseline 2: min freq Device Odroid XU3 Configuraion AI-framework: min uil = 0% AI-framework: min uil = 10% AI-framework: min uil = 30% AI-framework: min uil = 60% Power (wa) Laency (ms) AI-framework provides beer energy efficiency as discussed in Figure 10, he improvemen is small, ranging from 1% o 6%. Our analysis reveals ha since he Sunspider workload is CPU inensive, requiring more han 4 cores on average, he AI-framework does no have enough chance o apply DVFS. Noe ha hese wo devices have 4 cores, as explained in Table Pedesrian Deecion Workload Resuls. We measure he power consumpion and average deecion laency when we execue he pedesrian deecion workload on he Pine 64 andraspberrypi2device,presenedintable2.sincewecan uilize DVFS only in hese devices, we conduc experimens under hree configuraions. In he baseline 1, we configure all cores o run a he maximum frequency (1152 MHz for Pine 64 and 900 MHz for Raspberry Pi2). In baseline 2, all cores are configured o run a he minimum frequency (480 MHz for Pine 64 and 200 MHz for Raspberry Pi2). On he conrary, in he AI-framework, he frequency of a core is changed adapively based on he curren CPU uilizaion and he min/max uilizaion hreshold (20% and 80% in his experimen). Experimenal resuls show ha he AI-framework balances well beween he power consumpion and performance. Baseline 1 provides he bes performance a he cos of high power consumpion. On he conrary, baseline 2 reducespowerhemosbugivesanoiceableimpacon performance. However, our framework can reduce he power consumpion (22% reducion for Pine 64 and 5% reducion for Raspberry Pi2) while hardly affecing he performance of he workload. Table3showsheresulswhenweexecuehepedesrian deecion workload on he Odroid XU3 device. In his device, he AI-framework can uilize no only DVFS bu also DPM. Therefore, we conduc experimens wih four differen min_uilizaion hreshold values ha rigger he policy manager in our framework as discussed in Secion 3.4. When he hreshold becomes smaller, he AI-framework ries o apply low-power echniques conservaively, while applying echniques aggressively as he hreshold becomes larger. When he min uilizaion hreshold is se as 0%, he AI-framework ries o decrease compuing resources when he curren uilizaion is less han 0%. I means ha he AI-framework does no apply DPM and DVFS, urning on all cores wih maximum frequency, which provides he bes performance and he wors power consumpion in his device (baseline configuraion). When he hreshold is 10%, he AI-framework ries o decrease compuing resources conservaively, obaining relaively small power reducion (from 5.9 o 5.74 wa in his case). On he conrary, when he hreshold is 60%, i ries aggressively, yielding beer power reducion a he cos of laency. These resuls reveal he radeoff beween power reducion and performance. By seing he hreshold appropriaely (30% in his case), he AI-framework can reduce he power consumpion wihou considerable performance degradaion. 5. Conclusion In his paper, we design a new low-power framework for mulicore mobile devices. I inegraes boh DPM and DVFS echniques and applies hem adapively according oheworkloadcharacerisicsanddevicefeaures.real implemenaion based experimens show ha he proposed framework balances well beween he power consumpion and performance, resuling in he energy saving. We will exend our work ino he wo direcions. Firs, we invesigae he performance drop, especially for a CPU inensive workload observed in Figure 10, using hardwarelevel performance monioring uni suppored by processors. We conjecure ha workload-aware fine-grained power managemen can alleviae he drop while mainaining he power reducion benefi. The second direcion is developing a whaif engine ha can predic how an aleraion of frequency or number of acive cores influences energy efficiency in advance using our framework. Conflics of Ineres The auhors declare ha here are no conflics of ineres regarding he publicaion of his paper. Acknowledgmens The presen research was conduced by he research fund of Dankook Universiy (BK21 Plus) in 2014 and by he MSIP (Minisry of Science, ICT and Fuure Planning), Korea, under heitrc(informaiontechnologyresearchcener)suppor program (IITP-2016-R ) supervised by he IITP and by he Cener for Inegraed Smar Sensors funded by

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