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master's thesis
Poboljšanje performanci algoritama umjetne inteligencije uporabom ugradbenih sustava
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:532510
Hudiček, Damjan
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Hudiček, D. (2021). Poboljšanje performanci algoritama umjetne inteligencije uporabom ugradbenih sustava (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:532510

Hudiček, Damjan. "Poboljšanje performanci algoritama umjetne inteligencije uporabom ugradbenih sustava." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:532510

Hudiček, Damjan. "Poboljšanje performanci algoritama umjetne inteligencije uporabom ugradbenih sustava." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:532510

Hudiček, D. (2021). 'Poboljšanje performanci algoritama umjetne inteligencije uporabom ugradbenih sustava', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 12 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:532510

Hudiček D. Poboljšanje performanci algoritama umjetne inteligencije uporabom ugradbenih sustava [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 May 12] Available at: https://urn.nsk.hr/urn:nbn:hr:168:532510

D. Hudiček, "Poboljšanje performanci algoritama umjetne inteligencije uporabom ugradbenih sustava", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:532510

Metadata
TitlePoboljšanje performanci algoritama umjetne inteligencije uporabom ugradbenih sustava
Title (english)Performance improvement of artificial intelligence algorithms using embedded system
AuthorDamjan Hudiček
MentorVlado Sruk (mentor)
Committee memberVlado Sruk (predsjednik povjerenstva)
Committee memberDejan Škvorc (član povjerenstva)
Committee memberLeonardo Jelenković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-09-21, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Umjetna inteligencija u automobilskoj industriji. Automobil kao primjer sustava u kojem su ključne performance i niska potrošnja energije. Najpopularniji način ubrzanja algoritama umjetne inteligencije (GPU) u usporedbi s manje popularnim ali potencijalno efikasnijim (FPGA). Kratak pregled konvolucijskih i grafovskih neuronskih mreža. Mjerenje i usporedba performanci i potrošnje energije SqueezeNet konvolucijske neuronske mreže na Xilinx ZCU102 razvojnoj ploči i Nvidia GTX 1660 SUPER GPU.
Abstract (english)
Artificial intelligence in the automotive industry. Cars as an example of a performance focused and power constrained system. Comparing GPU and FGPA as two hardware acceleration platforms. Convolutional and graph neural networks. Testing and comparing performance and power efficiency of the SqueezeNet convolutional neural network on Xilinx ZCU102 evaluation board and Nvidia GTX 1660 SUPER GPU.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:532510
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-13 10:04:37