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General Processing on Graphics Processing Units for Industrial Systems
Lukačovič, Martin ; Mašek, Jan (referee) ; Krkoš, Radko (advisor)
The thesis deals with the abilities of graphics processors for GPGPU. It contains historical solutions to contemporary design. There are also described graphics processors from the largest manufacturers of this time, their focus and goals in the future. For algorithms implementation using GPU, there are necessary APIs that offer various possibilities of execution. In addition to the CPU and GPU universal heterogeneous computing, there are alternatives such as FPGA and DSP so it is necessary to consider the price and energy cost. Part of the work is devoted to the communication possibilities with the hardware and advanced memory approaches. For demonstrating parallel computing an implementation of matrix multiplication in OpenCL was realized.
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Image segmentation using deeplearning methods
Lukačovič, Martin ; Burget, Radim (referee) ; Mašek, Jan (advisor)
This thesis deals with the current methods of semantic segmentation using deep learning. Other approaches of neaural networks in the area of deep learning are also discussed. It contains historical solutions of neural networks, their development, and basic principle. Convolutional neural networks are nowadays the most preferable networks in solving tasks as detection, classification, and image segmentation. The functionality was verified on a freely available environment based on conditional random fields as recurrent neural networks and compered with the deep convolutional neural networks using conditional random fields as postprocess. The latter mentioned method has become the basis for training of new models on two different datasets. There are various enviroments used to implement neural networks using deep learning, which offer diverse perform possibilities. For demonstration purposes a Python application leveraging the BVLC\,/\,Caffe framework was created. The best achieved accuracy of a trained model for clothing segmentation is 50,74\,\% and 68,52\,\% for segmentation of VOC objects. The application aims to allow interaction with image segmentation based on trained models.
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Image segmentation using deeplearning methods
Lukačovič, Martin ; Burget, Radim (referee) ; Mašek, Jan (advisor)
This thesis deals with the current methods of semantic segmentation using deep learning. Other approaches of neaural networks in the area of deep learning are also discussed. It contains historical solutions of neural networks, their development, and basic principle. Convolutional neural networks are nowadays the most preferable networks in solving tasks as detection, classification, and image segmentation. The functionality was verified on a freely available environment based on conditional random fields as recurrent neural networks and compered with the deep convolutional neural networks using conditional random fields as postprocess. The latter mentioned method has become the basis for training of new models on two different datasets. There are various enviroments used to implement neural networks using deep learning, which offer diverse perform possibilities. For demonstration purposes a Python application leveraging the BVLC\,/\,Caffe framework was created. The best achieved accuracy of a trained model for clothing segmentation is 50,74\,\% and 68,52\,\% for segmentation of VOC objects. The application aims to allow interaction with image segmentation based on trained models.
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General Processing on Graphics Processing Units for Industrial Systems
Lukačovič, Martin ; Mašek, Jan (referee) ; Krkoš, Radko (advisor)
The thesis deals with the abilities of graphics processors for GPGPU. It contains historical solutions to contemporary design. There are also described graphics processors from the largest manufacturers of this time, their focus and goals in the future. For algorithms implementation using GPU, there are necessary APIs that offer various possibilities of execution. In addition to the CPU and GPU universal heterogeneous computing, there are alternatives such as FPGA and DSP so it is necessary to consider the price and energy cost. Part of the work is devoted to the communication possibilities with the hardware and advanced memory approaches. For demonstrating parallel computing an implementation of matrix multiplication in OpenCL was realized.
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