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Neural networks on AURIX platform
Smrčka, Michal ; Buchta, Luděk (referee) ; Blaha, Petr (advisor)
Part of this thesis discusses FNNs and how to develop them using MATLAB Deep Learning Toolbox and Keras API in Python. Subsequently, the thesis deals with the conversion of these networks into C/C++ using the Keras2c, AIfES, TFLM and NNoM libraries. This theoretical background was essential for the follow-up section, which focuses on the implementation and testing of the trained FNN on the AURIX TC397 3.3V Application Kit platform. This FNN is used to diagnose the PMS motor in order to detect inter turn faults. The configuration of the GPT12 and GETH peripherals of the AURIX TC397 microcontroller, which were used in the FNN testing application, is described in detail in this thesis. Using the Keras2c library, the possibility of running inference on 2 cores of the AURIX microcontroller was verified and quantization of the trained FNN was performed within the NNoM library. Finally, a comparison of the Keras2c, AIfES, TFLM and NNoM libraries was performed in terms of ease of implementation, classification accuracy and classification speed on the AURIX platform.
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Artificial intelligence on nVIDIA Jetson platform
Batelka, Lukáš ; Kozovský, Matúš (referee) ; Blaha, Petr (advisor)
The aim of this bachelor thesis is to design, train and implement an artificial neural network in an NVIDIA Jetson Nano embedded device. The first part of the thesis describes the current state of the art of implementing artificial intelligence in embedded devices. The following section describes the tools for developing artificial neural networks and the possibilities of implementing them in a Jetson Nano device. These tools are further used in the thesis to create and train an artificial neural network to detect a fault in preprocessed measurement data on a synchronous electric motor. Finally, the optimization of the trained neural network is described. The achieved results are summarized in the conclusion of the paper.
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Neural networks on AURIX platform
Smrčka, Michal ; Buchta, Luděk (referee) ; Blaha, Petr (advisor)
Part of this thesis discusses FNNs and how to develop them using MATLAB Deep Learning Toolbox and Keras API in Python. Subsequently, the thesis deals with the conversion of these networks into C/C++ using the Keras2c, AIfES, TFLM and NNoM libraries. This theoretical background was essential for the follow-up section, which focuses on the implementation and testing of the trained FNN on the AURIX TC397 3.3V Application Kit platform. This FNN is used to diagnose the PMS motor in order to detect inter turn faults. The configuration of the GPT12 and GETH peripherals of the AURIX TC397 microcontroller, which were used in the FNN testing application, is described in detail in this thesis. Using the Keras2c library, the possibility of running inference on 2 cores of the AURIX microcontroller was verified and quantization of the trained FNN was performed within the NNoM library. Finally, a comparison of the Keras2c, AIfES, TFLM and NNoM libraries was performed in terms of ease of implementation, classification accuracy and classification speed on the AURIX platform.
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Artificial intelligence on nVIDIA Jetson platform
Batelka, Lukáš ; Kozovský, Matúš (referee) ; Blaha, Petr (advisor)
The aim of this bachelor thesis is to design, train and implement an artificial neural network in an NVIDIA Jetson Nano embedded device. The first part of the thesis describes the current state of the art of implementing artificial intelligence in embedded devices. The following section describes the tools for developing artificial neural networks and the possibilities of implementing them in a Jetson Nano device. These tools are further used in the thesis to create and train an artificial neural network to detect a fault in preprocessed measurement data on a synchronous electric motor. Finally, the optimization of the trained neural network is described. The achieved results are summarized in the conclusion of the paper.
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