Computing Platforms For Deep Learning Task In Computer Vision
| but.event.date | 23.04.2020 | cs |
| but.event.title | Student EEICT 2020 | cs |
| dc.contributor.author | Kratochvila, Lukas | |
| dc.date.accessioned | 2021-07-15T13:12:39Z | |
| dc.date.available | 2021-07-15T13:12:39Z | |
| dc.date.issued | 2020 | cs |
| dc.description.abstract | The recent progress in machine learning in computer vision guides to enormous hardware requirements. This paper discovers new innovative hardware capable of dealing with immense demands. The important decision is concentrating on task learning or the final classification. The main concern is on five domains: single-board computers, hardware accelerators, graphics cards, workstations, and cloud computing. These devices have several key features for detection that are discussed. Cloud computing is another presented approach. Furthermore, different delivery models of cloud computing are addressed. | en |
| dc.format | text | cs |
| dc.format.extent | 171-175 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Proceedings II of the 26st Conference STUDENT EEICT 2020: Selected Papers. s. 171-175. ISBN 978-80-214-5868-0 | cs |
| dc.identifier.isbn | 978-80-214-5868-0 | |
| dc.identifier.uri | http://hdl.handle.net/11012/200647 | |
| dc.language.iso | en | cs |
| dc.publisher | Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.relation.ispartof | Proceedings II of the 26st Conference STUDENT EEICT 2020: Selected papers | en |
| dc.relation.uri | https://conf.feec.vutbr.cz/eeict/EEICT2020 | cs |
| dc.rights | © Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights.access | openAccess | en |
| dc.subject | Deep learning | en |
| dc.subject | Hardware | en |
| dc.subject | Cloud computing | en |
| dc.title | Computing Platforms For Deep Learning Task In Computer Vision | en |
| dc.type.driver | conferenceObject | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
| eprints.affiliatedInstitution.department | Fakulta elektrotechniky a komunikačních technologií | cs |
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