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Izvajanje naučenih globokih nevronskih mrež v vgrajenih sistemih
MLINAR GROZNIK, ANDREJ DAMJAN (Author), Bratko, Ivan (Mentor) More about this mentor... This link opens in a new window

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Abstract
Današnja razpoložljivost velikih računalniških zmogljivosti v obliki relativno poceni GPUjev in javno dostopni računalniški centri v oblaku omogočajo učenje velikih nevronskih mrež. Tudi vgrajeni sistemi so računalniško močnejši in cenejši, kar omogoča izvajanje večjih nevronskih mrež v le-teh. V okviru te diplomske naloge sem izbral in prikazal sestavne dele in metode za implementacijo globokih nevronskih mrež za razvrščanje slik, naučenih v oblaku in izvedenih v vgrajenih sistemih. V ta namen je bilo uporabljenih več vgrajenih sistemov z različno arhitekturo nevronskih mrež, pri tem pa sem primerjal njihove sposobnosti, zmogljivosti, uporabo virov, ceno in praktičnost uporabe. To služi kot vodilo za implementacijo sistemov za klasifikacijo slik na lahko dostopnih in nizkocenovnih vgrajenih sistemih.

Language:Slovenian
Keywords:globoke nevronske mreže, vgrajeni sistemi, umetna inteligenca, klasifikacija objektov
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2018
Views:587
Downloads:150
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Secondary language

Language:English
Title:Running trained deep neural networks on embedded systems
Abstract:
Today’s availability of enormous amounts of computational power in the form of relatively cheap GPUs and publicly accessible cloud computing facilities makes the training of large deep neural networks practical. Also embedded systems have been gaining in computational power and reducing their prices, making deployment of bigger neural networks on embedded systems feasible. In the scope of this diploma thesis the necessary components and methods for the implementation of deep neural networks for image classification trained on cloud computers and deployed on embedded systems are brought together and shown working. Several embedded systems were used with different neural network architectures and their capabilities, performance, resource usage, price and practicality compared. This serves as a guide to implement state of the art image classification on easily available and low cost embedded systems.

Keywords:deep neural networks, embedded systems, artificial intelligence, object classification

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