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Titlebook: Design and Architecture for Signal and Image Processing; 16th International W Miguel Chavarrías,Alfonso Rodríguez Conference proceedings 20

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Real-Time FPGA Implementation of the Semi-global Matching Stereo Vision Algorithm for a 4K/UHD Video/Ultra HD (. pixels @ 30 frames per second) video stream in a 4 pixel per clock (ppc) format and a 64-pixel disparity range. The baseline SGM implementation had to be modified to process pixels in the 4ppc format and meet the timing constrains, however, our version provides results comparable to the
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Adaptive Inference for FPGA-Based 5G Automatic Modulation Classificationizing AMC is crucial to achieving the latency, throughput, and energy levels expected by the final user. State-of-the-art solutions to the AMC problem are based on Deep Learning methods (e.g., Deep Neural Networks - DNNs). However, these methods require heavy processing and high energy consumption u
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Formal Methods: Foundations and Applicationsping scheduling task is accelerated at the potential expense of the produced schedule. Our method offers a limited and controlled decrease of the schedule quality while enabling mapping and scheduling execution time between 1 and 2 orders of magnitude faster than state-of-the-art techniques.
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Formal Verification of Neural Networks? hyperspectral video classification. The parameters of the operator along with the selection of the most suitable spectral band to process were chosen via optimization of the amount of vascular pixels detected and error metrics. The segmentation algorithm was implemented for both CPU and GPU platfor
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Rodrigo Otoni,Ana Cavalcanti,Augusto Sampaio the IP’s most power-influential operating modes and its inputs activity characteristics. The proposed online monitoring mechanism drastically reduces the communication-derived latency between the monitor and the DVFS. Experimental results show a mean absolute percentage error below . for the estima
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SCAPE: HW-Aware Clustering of Dataflow Actors for Tunable Scheduling Complexityping scheduling task is accelerated at the potential expense of the produced schedule. Our method offers a limited and controlled decrease of the schedule quality while enabling mapping and scheduling execution time between 1 and 2 orders of magnitude faster than state-of-the-art techniques.
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