Abstract
Becoming a ubiquitous part of a huge number of various applications, image processing algorithms and underling architectures have to meet many different requirements. Some have real-time performance constraints combined with demands on efficient implementation for limited or various hardware resources. This poses particular challenges for design, implementation, and evaluation of efficient image processing systems. In this paper, we present a model-based approach to address these issues using our framework SimTAny. Founded on the standard modeling language UML, we propose the UML Image Proccessing Language (UIPL) to facilitate expressing image processing application algorithms directly in UML, which is especially beneficial for rapid modeling. With the help of SimTAny, such design models can be simulated in order to investigate the performance of a modeled system, to determine optimal design solutions, and to validate the required properties. We extend SimTAny to enable the generation of efficient implementation code of image processing algorithms for different target architectures. The code generated is then directly integrated in the simulation environment to increase the accuracy of our performance evaluations.
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References
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Deitsch, A., Schneider, V. (2018). Model-Based System Design and Evaluation of Image Processing Architectures with SimTAny Framework. In: German, R., Hielscher, KS., Krieger, U. (eds) Measurement, Modelling and Evaluation of Computing Systems. MMB 2018. Lecture Notes in Computer Science(), vol 10740. Springer, Cham. https://doi.org/10.1007/978-3-319-74947-1_29
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DOI: https://doi.org/10.1007/978-3-319-74947-1_29
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