Titlebook: Designing Modern Embedded Systems: Software, Hardware, and Applications; 7th IFIP TC 10 Inter Stefan Henkler,Márcio Kreutz,Achim Rettberg C

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Space-Fluid and Time-Fluid Programming of such systems is a key criterion for their efficiency, regular array-type architectures are preferred that can easily grow in size. In this work, we model in SystemC TLM-2.0 a Grid of Processing Cells (GPC) with a Checkerboard arrangement of processors and memories. To demonstrate its scalability

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Space-Fluid and Time-Fluid Programminglications (MVMs) with Resistive Random Access Memory (ReRAM) crossbars has paved the way for solving the memory bottleneck issues related to LSTM processing. However, mixed-signal and fully-analog accelerators still lack in developing energy-efficient and versatile devices for the calculus of activa

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Turbulence in Open-Channel Flows,his phenomenon is well known as the memory bottleneck and is a great challenge in computer engineering. In order to mitigate the memory bottleneck in classic multi-core architectures, a scalable parallel computing platform called Grid of Processing Cells (GPC) has been proposed. To evaluate its effe

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Dimensional Analysis and Similitude,ded systems. In this paper, we focus on the analysis of NNs to find correlations between their characteristics in order to be reliable and predictable in time, with the aim of making sugar beet recognition more transparent. Although obtaining promising results, as we are only going to focus on analy

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Fluvial Processes: Meandering and Braiding,learning models. For plant classification and monitoring, it is easier to collect data of healthy plants than it is to collect data of plants that are infected by various diseases, because they are simply more common. Sufficient data are therefore often lacking for the accurate detection of diseased

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Methodisches Vorgehen II: Datenkorpus Tucker decomposition in order to optimize a given convolutional neural network for its parameter count and thus inference performance on embedded systems. TC enables a quick generation of the next instance in the NAS process, avoiding the need for a time consuming full training after each step. We

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