First results of a comparison of machine learning hardware acceleration approaches using field programmable gate arrays in an agricultural mobile robotic application case

Publisher DOI:	10.1007/978-3-031-60023-4_8
Title:	First results of a comparison of machine learning hardware acceleration approaches using field programmable gate arrays in an agricultural mobile robotic application case
Language:	English
Authors:	Tiedemann, Tim Schmidt, Anja Stricker, Jesse Fuhrmann, Jonas
Editor:	Köhler-Bußmeier, Michael Renz, Wolfgang Sudeikat, Jan
Issue Date:	1-Jul-2024
Publisher:	Springer
Part of Series:	Intelligent Distributed Computing XVI : 16th International Symposium on Intelligent Distributed Computing, IDC 2023
Journal or Series Name:	Studies in computational intelligence
Volume:	1138
Startpage:	27
Endpage:	39
Project:	Robotik in der Landwirtschaft
Conference:	International Symposium on Intelligent Distributed Computing 2023
URI:	https://hdl.handle.net/20.500.12738/16043
ISBN:	978-3-031-60023-4 978-3-031-60022-7
ISSN:	1860-9503
Review status:	This version was peer reviewed (peer review)
Institute:	Department Informatik Department Informations- und Elektrotechnik Fakultät Technik und Informatik Forschungs- und Transferzentrum Smart Systems
Type:	Chapter/Article (Proceedings)
Additional note:	Funded by the Federal Ministry of Food and Agriculture (BMEL) by a decision of the German Bundestag. The Federal Offce for Agriculture and Food (BLE) provides coordinating support for artificial intelligence (AI) in agriculture as funding organization, grant number 28DK103C20.
Funded by:	Bundesministerium für Ernährung und Landwirtschaft
Appears in Collections:	Publications without full text

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