Publication:

cREAtIve: Reconfigurable Embedded Artificial Intelligence Invited Paper

Date

2021
Proceedings Paper
https://doi.org/10.1145/3457388.3458857
Simple item page Full metadata Statistics
 
cris.virtual.department#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.department#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.orcid0000-0003-1273-6739
cris.virtual.orcid0000-0002-9373-1210
cris.virtualsource.department4cae0a8b-913e-4035-ba86-41ae7330016f
cris.virtualsource.department148b397c-9b4c-4819-af84-5c5b57857d8d
cris.virtualsource.orcid4cae0a8b-913e-4035-ba86-41ae7330016f
cris.virtualsource.orcid148b397c-9b4c-4819-af84-5c5b57857d8d
dc.contributor.authorBahrebar, Poona
dc.contributor.authorDenis, Leon
dc.contributor.authorBonnaerens, Maxim
dc.contributor.authorCoddens, Kristof
dc.contributor.authorDambre, Joni
dc.contributor.authorFavoreel, Wouter
dc.contributor.authorKhvastunov, Illia
dc.contributor.authorMunteanu, Adrian
dc.contributor.authorHung Nguyen-Duc
dc.contributor.authorSchulte, Stefan
dc.contributor.authorStroobandt, Dirk
dc.contributor.authorValvekens, Ramses
dc.contributor.authorVan den Broeck, Nick
dc.contributor.authorVerbruggen, Geert
dc.contributor.imecauthorBonnaerens, Maxim
dc.contributor.imecauthorDambre, Joni
dc.contributor.orcidextDenis, Leon::0000-0001-7564-5549
dc.contributor.orcidextSchulte, Stefan::0000-0001-6828-9945
dc.contributor.orcidextVan den Broeck, Nick::0000-0001-5900-4813
dc.contributor.orcidimecDambre, Joni::0000-0002-9373-1210
dc.contributor.orcidimecBonnaerens, Maxim::0000-0003-1273-6739
dc.date.accessioned2023-06-22T14:39:06Z
dc.date.available2023-06-20T10:39:43Z
dc.date.available2023-06-20T12:27:29Z
dc.date.available2023-06-22T14:39:06Z
dc.date.embargo2021-05-31
dc.date.issued2021
dc.description.abstractcREAtIve targets the development of novel highly-adaptable embedded deep learning solutions for automotive and traffic monitoring applications, including position sensor processing, scene interpretation based on LiDAR, and object detection and classification in thermal images for traffic camera systems. These applications share the need for deep learning solutions tailored for deployment on embedded devices with limited resources and featuring high adaptability and robustness to changing environmental conditions. cREAtIve develops knowledge, tools and methods that enable hardware-efficient, adaptable, and robust deep learning.
dc.identifier.doi10.1145/3457388.3458857
dc.identifier.eisbn978-1-4503-8404-9
dc.identifier.isbn9781450384049
dc.identifier.issn/
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/42064
dc.publisherASSOC COMPUTING MACHINERY
dc.source.beginpage194
dc.source.conference18th ACM International Conference on Computing Frontiers (CF)
dc.source.conferencedate2021-05-11
dc.source.conferencelocationOnline
dc.source.endpage199
dc.source.journalCF '21: Computing Frontiers Conference
dc.source.numberofpages6
dc.title

cREAtIve: Reconfigurable Embedded Artificial Intelligence Invited Paper

dc.typeProceedings paper
dspace.entity.typePublication
Files

Original bundle

Name:
3457388.3458857.pdf
Size:
2.55 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download
Publication available in collections:
Conference contributions