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Intelligent Data Processing using In-Orbit Advanced Algorithms on Heterogeneous System Architecture
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-8096-3891
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1687-930X
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-7586-0409
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-8785-5380
2018 (English)In: IEEE Aerospace Conference 2018 IEEEAC2018, 2018, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, commercial exploitation of small satellites and CubeSats has rapidly increased. Time to market of processed customer data products is becoming an important differentiator between solution providers and satellite constellation operators. Timely and accurate data dissemination is the key to success in the commercial usage of small satellite constellations which is ultimately dependent on a high degree of autonomous fleet management and automated decision support. The traditional way for disseminating data is limited by on the communication capability of the satellite and the ground terminal availability. Even though cloud computing solutions on the ground offer high analytical performance, getting the data from the space infrastructure to the ground servers poses a bottleneck of data analysis and distribution. On the other hand, adopting advanced and intelligent algorithms onboard offers the ability of autonomy, tasking of operations, and fast customer generation of low latency conclusions, or even real-time communication with assets on the ground or other sensors in a multi-sensor configuration. In this paper, the advantages of intelligent onboard processing using advanced algorithms for Heterogeneous System Architecture (HSA) compliant onboard data processing systems are explored. The onboard data processing architecture is designed to handle a large amount of high-speed streaming data and provides hardware redundancy to be qualified for the space mission application domain. We conduct an experimental study to evaluate the performance analysis by using image recognition algorithms based on an open source intelligent machine library 'MIOpen' and an open standard 'OpenVX'. OpenVX is a cross-platform computer vision library.

Place, publisher, year, edition, pages
2018. p. 1-8
Series
IEEE Aerospace Conference Proceedings, ISSN 1095-323X
Keywords [en]
Heterogeneous System Architecture (HSA)Intelligent Data ProcessingMIOpenOpenVXCubeSatCPU-GPUEnergy consumption
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-38628DOI: 10.1109/AERO.2018.8396536Scopus ID: 2-s2.0-85049840022OAI: oai:DiVA.org:mdh-38628DiVA, id: diva2:1188079
Conference
IEEE Aerospace Conference 2018 IEEEAC2018, 03 Mar 2018, Big Sky, United States
Projects
DPAC - Dependable Platforms for Autonomous systems and ControlAvailable from: 2018-03-06 Created: 2018-03-06 Last updated: 2019-11-11Bibliographically approved
In thesis
1. Improving On-Board Data Processing using CPU-GPU Heterogeneous Architectures for Real-Time Systems
Open this publication in new window or tab >>Improving On-Board Data Processing using CPU-GPU Heterogeneous Architectures for Real-Time Systems
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis investigates the efficacy of heterogeneous computing architectures in real-time systems.The goals of the thesis are twofold. First, to investigate various characteristics of the Heterogeneous System Architectures (HSA) compliant reference platforms focusing on computing performance and power consumption. The investigation is focused on the new technologies that could boost on-board data processing systems in satellites and spacecraft. Second, to enhance the usage of the heterogeneous processing units by introducing a technique for static allocation of parallel segments of tasks.

The investigation and experimental evaluation show that our method of GPU allocation for the parallel segments of tasks is more energy efficient compared to any other studied allocation. The investigation is conducted under different types of environments, such as process-level isolated environment, different software stacks, including kernels, and various task set scenarios. The evaluation results indicate that a balanced use of heterogeneous processing units (CPU and GPU) could improve schedulability of task sets up to 90% with the proposed allocation technique.

Abstract [sv]

Denna avhandling undersöker effektiviteten hos heterogena datorarkitekturer i realtidssystem. Målet med avhandlingen är tvåfaldigt. Till att börja med, att undersöka olika egenskaper hos plattformar baserade på Heterogeneous System Architecture, med fokus på datorprestanda och strömförbrukning. Undersökningen är inriktad på tekniker som kan öka datorbehandlingssystemen ombord i satelliter och rymdskepp. För det andra förbättra användningen av heterogena arkitekturer genom att införa en teknik för statisk allokering av parallella programsegment.

Undersökningen och den experimentella utvärderingen visar att vår metod för effektiv användning av GPU-allokering för parallella programsegment är den mest energieffektiva jämfört med någon annan studerad allokering. Undersökningarna har genomförts i olika typer av miljöer, såsom processisolerad miljö, olika mjukvarustackar, inklusive kernel, och olika uppsättningsscenarier. Utvärderingsresultaten indikerar dessutom att en balanserad användning av heterogena beräkningsenheter (CPU och GPU) kan förbättra schemaläggningen för vissa program upp till 90% jämfört med de tidigare föreslagna allokeringsteknikerna.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 286
Keywords
on-board data processing, CPU-GPU, heterogeneous architectures, real-time systems
National Category
Engineering and Technology Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-45940 (URN)978-91-7485-450-3 (ISBN)
Presentation
2019-12-18, Kappa, Mälardalens högskola, Västerås, 09:15 (English)
Opponent
Supervisors
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-19Bibliographically approved

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Tsog, NandinbaatarBehnam, MorisNolin, MikaelBruhn, Fredrik

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