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TinyLid: a RISC-V accelerated Neural Network For LiDAR Cont..:

, In: Proceedings of the 21st ACM International Conference on Computing Frontiers,

Jati, Grafika ; Molan, Martin ; Barchi, Francesco.. - p. 249-257 , 2024

Link: https://dl.acm.org/doi/10.1145/3649153.3649201

RT 
T1 <cTitle>Proceedings of the 21st ACM International Conference on Computing Frontiers</cTitle>: T1 <Title>TinyLid: a RISC-V accelerated Neural Network For LiDAR Contaminant Classification in Autonomous Vehicle</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3649201&Exemplar=1&LAN=DE
				
A1 Jati, Grafika
A1 Molan, Martin
A1 Barchi, Francesco
A1 Bartolini, Andrea
A1 Acquaviva, Andrea
PB ACM
YR 2024
K1 Advanced driver assistance systems (ADAS)
K1 GAP8
K1 LiDAR contamination classification
K1 LiDAR reliability
K1 RISC-V
K1 TinyML
K1 autonomous vehicle perception systems
K1 autonomous vehicle safety
K1 Computer systems organization
K1 Embedded and cyber-physical systems
K1 Sensors and actuators
K1 Embedded systems
K1 Embedded software
K1 Computing methodologies
K1 Artificial intelligence
K1 Computer vision
K1 Computer vision tasks
K1 Scene anomaly detection
K1 Hardware
K1 Communication hardware, interfaces and storage
K1 Sensor applications and deployments
K1 Image and video acquisition
K1 3D imaging
K1 Computer vision representations
K1 Appearance and texture representations
K1 Vision for robotics
SP 249
OP 257
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3649153.3649201
DO https://dl.acm.org/doi/10.1145/3649153.3649201
SF ELIB - SuUB Bremen

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