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  1. Pubblicazioni

The DeepHealth Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous HPC and Cloud Architectures

Capitolo di libro
Data di Pubblicazione:
2021
Citazione:
The DeepHealth Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous HPC and Cloud Architectures / Aldinucci, M., Atienza, D., Bolelli, F., Caballero, M., Colonnelli, I., Flich, J., Gómez, J.A., González, D., Grana, C., Grangetto, M., Leo, S., López, P., Oniga, D., Paredes, R., Pireddu, L., Quiñones, E., Silva, T., Tartaglione, E., Zapater, M. - In: Technologies and Applications for Big Data Value / [a cura di] Edward Curry, Sören Auer, Arne J. Berre, Andreas Metzger, Maria S. Perez, Sonja Zillner. - Cham : Springer, 2021. - ISBN 9783030783068. - pp. 183-202 [10.1007/978-3-030-78307-5_9]
Abstract:
At the present time, we are immersed in the convergence between Big Data, High-Performance Computing and Artificial Intelligence. Technological progress in these three areas has accelerated in recent years, forcing different players like software companies and stakeholders to move quicky. The European Union is dedicating a lot of resources to maintain its relevant position in this scenario, funding projects to implement large-scale pilot testbeds that combine the latest advances in Artificial Intelligence, High-Performance Computing, Cloud and Big Data technologies. The DeepHealth project is an example focused on the health sector whose main outcome is the DeepHealth toolkit, a European unified framework that offers deep learning and computer vision capabilities, completely adapted to exploit underlying heterogeneous High-Performance Computing, Big Data and cloud architectures, and ready to be integrated into any software platform to facilitate the development and deployment of new applications for specific problems in any sector. This toolkit is intended to be one of the European contributions to the field of AI. This chapter introduces the toolkit with its main components and complementary tools; providing a clear view to facilitate and encourage its adoption and wide use by the European community of developers of AI-based solutions and data scientists working in the healthcare sector and others.
Tipologia CRIS:
Capitolo/Saggio
Keywords:
Hardware-specific capabilities for big data GPUs FPGAs; High performance data analytics; Hybrid big data HPC architectures; Performance for large-scale processing;
Elenco autori:
Aldinucci, Marco; Atienza, David; Bolelli, Federico; Caballero, Mónica; Colonnelli, Iacopo; Flich, José; Gómez, Jon A.; González, David; Grana, Costantino; Grangetto, Marco; Leo, Simone; López, Pedro; Oniga, Dana; Paredes, Roberto; Pireddu, Luca; Quiñones, Eduardo; Silva, Tatiana; Tartaglione, Enzo; Zapater, Marina
Autori di Ateneo:
BOLELLI FEDERICO
GRANA Costantino
Link alla scheda completa:
https://iris.unimore.it/handle/11380/1230906
Link al Full Text:
https://iris.unimore.it//retrieve/handle/11380/1230906/420166/2021bdva.pdf
Titolo del libro:
Technologies and Applications for Big Data Value
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