Print Email Facebook Twitter Optimising quality-of-control for data-intensive multiprocessor image-based control systems considering workload variations Title Optimising quality-of-control for data-intensive multiprocessor image-based control systems considering workload variations Author Mohamed, S. Zhu, D. Goswami, D. Basten, A.A. Contributor Konofaos, N. (editor) Novotny, M. (editor) Skavhaug, A. (editor) Publication year 2018 Abstract Image-Based Control (IBC) systems have a long sample period. Sensing in these systems consists of compute-intensive image processing algorithms whose response times are dependent on image workload. IBC systems are typically designed for the worst-case workload that results in a long sample period and hence suboptimal quality-of-control (QoC). This worst-case based design is further considered for mapping of controller tasks and allocating platform resources, resulting in significant resource over-provisioning. Our design philosophy is to sample as fast as possible to optimise QoC for a given platform allocation, and for this, we present a structured design flow. Workload variations determine how fast we can sample and we model this dynamic behaviour using the concept of workload scenarios. Our choice of scenario-aware dataflow as the formal model for our application enables us to: i) model dynamic behaviour, analyse timing, and optimally map application tasks to the platform for maximising the effective utilisation of allocated resources, ii) relate throughput of the dataflow graph to the sample period, and thus combine dataflow analysis and mapping with control design parameters and QoC to identify system scenarios, and iii) to efficiently implement a run-time mechanism that manages necessary dynamic reconfiguration between system scenarios. Our results show that our design approach outperforms the worst-case based design with respect to optimising QoC and maximising effective resource utilisation. © 2018 IEEE. Subject Co-designImage Based ControlMappingModel Based DesignScenario AwareData flow analysisDynamic modelsEmbedded systemsImage processingMultiprocessing systemsProduct designSystems analysisImage-based controlModel-based designsMultiprocessorScenario AwareQuality control To reference this document use: http://resolver.tudelft.nl/uuid:67dd4ddf-d958-4c3d-9b4d-447829230468 TNO identifier 843745 Publisher Institute of Electrical and Electronics Engineers Inc. ISBN 9781538673768 Source Proceedings - 21st Euromicro Conference on Digital System Design, DSD 2018, 29 August 2018 through 31 August 2018, 320-327 Article number 8491834 Bibliographical note Conference Paper Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.