Grelck, C.

Roeder, J.

Rouxel, B.

Schultz, U.P.

Seewald, A.

English

Computational energy-efficiency is a critical aspect of many modern embedded devices as it impacts the level of autonomy for numerous scenarios. We present a component-based energy modeling approach to abstract per-component energy in a dataflow computational network executed according to a given scheduling policy. The approach is based on a modeling tool and ultimately relies on battery state to support a wider range of energy-optimization strategies for power-critical devices

International Migration, Integration and Social Cohesion online publications

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Component-based computation-energy modeling for embedded systemsSeewald, A.; Schultz, U.P.; Roeder, J.; Rouxel, B.; Grelck, C.DOI10.1145/3359061.3362775Publication date2019Document VersionFinal published versionPublished inSPLASH companion '19Link to publicationCitation for published version (APA):Seewald, A., Schultz, U. P., Roeder, J., Rouxel, B., & Grelck, C. (2019). Component-basedcomputation-energy modeling for embedded systems. In Y. Smaragdakis (Ed.), SPLASHcompanion '19: proceedings companion of the 2019 ACM SIGPLAN International Conferenceon Systems, Programming, Languages, and Applications: Software for Humanity : October20-25, 2019, Athens, Greece (pp. 5-6). Association for Computing Machinery.https://doi.org/10.1145/3359061.3362775General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:10 Mar 2023Component-Based Computation-EnergyModeling for Embedded SystemsAdam SeewaldUlrik Pagh Schultzads,ups@mmmi.sdu.dkUniversity of Southern DenmarkDenmarkJulius RoederBenjamin RouxelClemens Grelckj.roeder,b.a.l.rouxel,c.grelck@uva.nlUniversity of AmsterdamNetherlandsAbstractComputational energy–efficiency is a critical aspect of manymodern embedded devices as it impacts the level of auton-omy for numerous scenarios.We present a component–basedenergy modeling approach to abstract per–component en-ergy in a dataflow computational network executed accord-ing to a given scheduling policy. The approach is based on amodeling tool and ultimately relies on battery state to sup-port a wider range of energy–optimization strategies forpower–critical devices.CCS Concepts • Hardware → Power estimation andoptimization; • Computing methodologies→Modelingand simulation; •Computer systems organization→ Em-bedded systems; Multicore architectures.Keywords Energy Profiling, Energy Modeling, EmbeddedPlatforms, Component-Based DevelopmentACM Reference Format:Adam Seewald, Ulrik Pagh Schultz, Julius Roeder, Benjamin Rouxel,and Clemens Grelck. 2019. Component-Based Computation-EnergyModeling for Embedded Systems. In Proceedings of the 2019 ACMSIGPLAN International Conference on Systems, Programming, Lan-guages, and Applications: Software for Humanity (SPLASH Compan-ion ’19), October 20–25, 2019, Athens, Greece. ACM, New York, NY,USA, 2 pages. https://doi.org/10.1145/3359061.33627751 IntroductionEnergy modeling for complex unpredictable embedded sys-tems can be a challenging task. Modern embedded architec-tures range from microcontroller powered devices to hetero-geneous platforms executing parallel programs on differentcores and computational units. Application–side, parallelPermission to make digital or hard copies of part or all of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contactthe owner/author(s).SPLASH Companion ’19, October 20–25, 2019, Athens, Greece© 2019 Copyright held by the owner/author(s).ACM ISBN 978-1-4503-6992-3/19/10.https://doi.org/10.1145/3359061.3362775software featuring independent tasks runs as a result of dif-ferent components being executed together. A component–based approach can significantly reduce development timeand overall complexity. Althoughmanymodern systems ben-efit from it by, e.g., ensuring better scalability over time, itsadoption in the embedded domain remains marginal [4].Indeed, energy efficiency, time, security, and other non–functional properties requirements still pose a major obstacleto its adoption [1].We propose a component–based energy modeling ap-proach to assess the energy efficiency of power critical de-vices, built upon our previous work on coarse–grained mod-eling [7]. Coarse–grained modeling consists of profiling adiscrete set of configurations by varying different compo-nent configuration parameters, measuring the overall powerconsumption, and approximating any missing values. Theproposed approach handles computational aspects of hetero-geneous hardware. For instance, it takes into account but isnot limited to, different computational units such as CPUand GPU. For the software, it models an arbitrary numberof components interacting together. The approach can be ofparticular interest in an energy–aware scheduling technique,where a scheduler varies non–functional properties based onthe generated energy model. As an example, the developerspecifies application–level energy and a worst–case execu-tion time measure. The scheduler generates the best possibleschedule and configuration that meets the requirements.Our work is part of the TeamPlay project, that aims todevelop formally–motivated techniques to address non–fun-ctional properties satisfiability [1], and is implemented bya profiling tool named powprofiler distributed under MITlicense (https://bitbucket.org/adamseew/powprofiler). Thecurrent implementation builds an energy model designedfor the optimization of computational energy, but it can beused in a future setup with a scheduler to potentially exploitoptimized planning decisions such as dynamic and staticoptimal scheduling.2 Energy ModelsIn the embedded systems domain, computational energy istraditionally modeled to lower power consumption. System–level configurations are among the most used [3], while some5SPLASH Companion ’19, October 20–25, 2019, Athens, Greece Adam Seewald, Ulrik Pagh Schultz, Julius Roeder, Benjamin Rouxel, and Clemens Grelckapproaches focus on optimal chip configuration [5]. Othersinclude different computational units [2], rely on machinelearning techniques [8], and some focus on a coarse–grainedmodeling [7].Energy models are of particular interest for mobile robots,as they often present a constrained power budget. To thisextent, a drone use case featuring pattern recognition onopen water based on a coarse–grained component modelwas developed. Different components, like edge detectionand base station communication, cooperate such that animage is stored for further analysis if an object is detectedby the drone flying over the sea. The modeling strategyconsists of defining different acceptable QoS parameters percomponent formally defined as non–functional properties.2.1 Component-Based ModelingTo address the possible uncertainty resulting from runningcomponents in a wider, dependency–constrained network,our approach relies on a component–based modeling tech-nique. A simple static scheduling algorithm [6] that runs com-ponents sequentially while calling powprofiler is generatedsuch that the scheduling algorithm executes componentsindividually at profile–time. In particular, when a given com-ponent executes, a profiling thread is invoked through thecallback powprof_start, along with the configuration thatthe profiling corresponds to. The scheduler similarly invokesthe callback powprof_stop to stop the profiling thread. In oursystem, dependencies between components are handled bythe scheduler during profiling, such that components onlyexecute once all of their inputs are available. In fact, in a com-plex application, the execution of a given component may of-ten influence the energy consumed by others. All the profileddata are used to generate an energy model per–componentlater saved in a non–functional properties definition file forthe subsequent use by the TeamPlay toolchain. Base energy,that consists of profiling the system without componentsfor a certain amount of time, can be subtracted from theper–component energy model to obtain a component costvalue.Per–component energy can be used by the scheduler toselect different implementations of components to meet anenergy budget. The approach can considerably reduce profil-ing time and will potentially consist of adding costs one ontop of each other to model components in parallel within cer-tain system–specific energy boundaries. The resulting value,even if potentially higher as it might include the energy costof the system utilities along with the components, can bestill a valid approximation.2.2 From Abstraction to Real BehaviorIn the current setup, when evaluating an energy model per–component in a wider dataflow computational network, themodel can approximate energy consumption of the networkwith the cost values. Both coarse–grained (presented in ourprevious work) and component–based approaches can beused as a basis for a wider power modeling decision. Thecurrent approach is faster in execution and simpler fromthe developer side, as it requires less effort while helping todetermine the best possible configuration within the energyand QoS constraints. Its validity can be assessed by an equiv-alent coarse–grained energy model more closely related toreal behavior since it was validated against a fine–grainedmodel and showed a close relation.2.3 Battery State of ChargeA robust correlation in powprofiler to real scenarios is achie-ved by modeling the battery state of charge (SoC), as an ab-straction for handling energy efficiency of power–criticaldevices. SoC–featured model can be used for an energy–efficient scheduling policy as an optimal policy can consid-erably impact battery life. For instance, a stable power drainof components scheduled in a battery aware fashion candrain less energy, as distributing the same computation ina smaller number of power–intensive operations. A utilitythat correlates the SoC to the optimal configuration with anoptimization algorithm is considered future work.AcknowledgmentsThis work is supported and partly funded by the EuropeanUnion’s Horizon2020 research and innovation program un-der grant agreement No. 779882 (TeamPlay).References[1] 2019. Public deliverables of the TeamPlay Horizon2020 project. https://teamplay-h2020.eu/index.php?page=deliverables. Accessed: 2019-08-25.[2] Peter E Bailey, David K Lowenthal, Vignesh Ravi, Barry Rountree, Mar-tin Schulz, and Bronis R De Supinski. 2014. Adaptive configurationselection for power-constrained heterogeneous systems. In 2014 43rdInternational Conference on Parallel Processing. IEEE, 371–380.[3] Princey Chowdhury and Chaitali Chakrabarti. 2005. Static task-scheduling algorithms for battery-powered DVS systems. IEEE transac-tions on very large scale integration (VLSI) systems 13, 2 (2005), 226–237.[4] Ivica Crnkovic. 2005. Component-based software engineering for em-bedded systems. In Proceedings of the 27th international conference onSoftware engineering. ACM, 712–713.[5] Ami Marowka. 2017. Energy-aware modeling of scaled heterogeneoussystems. International Journal of Parallel Programming 45, 5 (2017),1026–1045.[6] Benjamin Rouxel, Roeder Julius, Altmeyer Sebastian, and GrelckClemens. 2019. A time, energy and security coordination approach.In 10th International Workshop on Analysis Tools and Methodologies forEmbedded and Real-Time Systems (WATERS 2019).[7] Adam Seewald, Ulrik P Schultz, Emad Ebeid, and Henrik S Midtiby.2019. Coarse-grained computation-oriented energy modeling for het-erogeneous parallel embedded systems. (2019). Manuscript submittedfor publication.[8] Gene Wu, Joseph L Greathouse, Alexander Lyashevsky, NuwanJayasena, and Derek Chiou. 2015. GPGPU performance and powerestimation using machine learning. In 2015 IEEE 21st International Sym-posium on High Performance Computer Architecture (HPCA). IEEE, 564–576.6

Component-based computation-energy modeling for embedded systems

https://pure.uva.nl/ws/files/55286346/3359061.3362775.pdf

Component-based computation-energy modeling for embedded systems

Abstract

Similar works

Full text

Available Versions

International Migration, Integration and Social Cohesion online publications