Convolutional Neural Networks on Embedded Automotive Platforms: A Qualitative Comparison

Brilli, Gianluca; Burgio, Paolo; Bertogna, Marko

doi:10.1109/HPCS.2018.00084

In the last decade, the rise of power-efficient, het- erogeneous embedded platforms paved the way to the effective adoption of neural networks in several application domains. Especially, many-core accelerators (e.g., GPUs and FPGAs) are used to run Convolutional Neural Networks, e.g., in autonomous vehicles, and industry 4.0. At the same time, advanced research on neural networks is producing interesting results in computer vision applications, and NN packages for computer vision object detection and categorization such as YOLO, GoogleNet and AlexNet reached an unprecedented level of accuracy and perfor- mance. With this work, we aim at validating the effectiveness and efficiency of most recent networks on state-of-the-art embedded platforms, with commercial-off-the-shelf System-on-Chips such as the NVIDIA Tegra X2 and Xilinx Ultrascale+. In our vision, this work will support the choice of the most appropriate CNN package and computing system, and at the same time tries to “make some order” in the field.

Data di pubblicazione:	2018
Titolo:	Convolutional Neural Networks on Embedded Automotive Platforms: A Qualitative Comparison
Autore/i:	Brilli, Gianluca; Burgio, Paolo; Bertogna, Marko
Autore/i UNIMORE:	BRILLI, GIANLUCA BURGIO, PAOLO BERTOGNA, Marko
Digital Object Identifier (DOI):	10.1109/HPCS.2018.00084
Nome del convegno:	International Conference on High Performance Computing & Simulation
Luogo del convegno:	Orleans, France
Data del convegno:	16 luglio 2018
Pagina iniziale:	496
Pagina finale:	499
Citazione:	Convolutional Neural Networks on Embedded Automotive Platforms: A Qualitative Comparison / Brilli, Gianluca; Burgio, Paolo; Bertogna, Marko. - (2018), pp. 496-499. ((Intervento presentato al convegno International Conference on High Performance Computing & Simulation tenutosi a Orleans, France nel 16 luglio 2018.
Tipologia	Relazione in Atti di Convegno

File in questo prodotto:

File	Descrizione	Tipologia
brilli_HPCS18.pdf		Versione editoriale	Open Access Visualizza/Apri

Utilizza questo identificativo per citare o creare un link a questo documento:
http://hdl.handle.net/11380/1169251

Citazioni

I documenti presenti in Iris Unimore sono rilasciati con licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia, salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris