Thesis Details
Akcelerované neuronové sítě na grafické kartě
This thesis deals with the implementation of an application for artificial neural networks simulation and acceleration using a graphics processing unit. The computation and training of feedforward neural networks using the Backpropagation algorithm are the main focus of this thesis, but the application also supports other network types, and it makes it possible to extend the application with different training algorithms. Next, the application allows us to create neural networks with structural anomalies, and thus, to test the neural network's fault tolerance. The application is implemented in the C++ language, using OpenCL to manage GPU computation. The Backpropagation acceleration results were compared with the free open source library FANN.
Neural networks, machine learning, acceleration, GPU, OpenCL, C++
Kořenek Jan, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Květoňová Šárka, Ing., Ph.D. (DIFS FIT BUT), člen
Španěl Michal, Ing., Ph.D. (DCGM FIT BUT), člen
Zbořil František, doc. Ing., Ph.D. (DITS FIT BUT), člen
@bachelorsthesis{FITBT17187, author = "Martin Tomko", type = "Bachelor's thesis", title = "Akcelerovan\'{e} neuronov\'{e} s\'{i}t\v{e} na grafick\'{e} kart\v{e}", school = "Brno University of Technology, Faculty of Information Technology", year = 2015, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/17187/" }