Reference : Compression of Deep Neural Networks for Space Autonomous Systems

	Document type :	Scientific congresses, symposiums and conference proceedings : Poster
	Discipline(s) :	Physical, chemical, mathematical & earth Sciences : Space science, astronomy & astrophysics Engineering, computing & technology : Computer science Engineering, computing & technology : Multidisciplinary, general & others
	Focus Areas :	Security, Reliability and Trust; Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/56045

Title :	Compression of Deep Neural Networks for Space Autonomous Systems
Language :	English
Author, co-author :	Shneider, Carl [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
	Sinha, Nilotpal [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
	Jamrozik, Michele Lynn [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
	Astrid, Marcella [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
	Rostami Abendansari, Peyman [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
	Kacem, Anis [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
	Shabayek, Abd El Rahman [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
	Aouada, Djamila [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
Publication date :	19-Apr-2023
Number of pages :	A0
Peer reviewed :	No
Audience :	International
Event name :	Luxembourg Space Resources Week 2023
Event date :	19-04-2023 to 21-04-2023
Event place (city) :	Luxembourg
Event country :	Luxembourg
Keywords :	[en] Deep Learning ; Neural Network Compression ; Edge Devices
Abstract :	[en] Efficient compression techniques are required to deploy deep neural networks (DNNs) on edge devices for space resource utilization tasks. Two approaches are investigated.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > CVI² - Computer Vision Imaging & Machine Intelligence
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	Enabling Learning And Inferring Compact Deep Neural Network Topologies On Edge Devices (ELITE)
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/56045
DOI :	10.5281/zenodo.7931156
Other URL :	https://zenodo.org/record/7931156
FnR project :	FnR ; FNR15965298 > Djamila Aouada > ELITE > Enabling Learning And Inferring Compact Deep Neural Network Topologies On Edge Devices > 01/04/2022 > 31/03/2025 > 2021

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