https://doi.org/10.1051/epjconf/202429508014
Scalable training on scalable infrastructures for programmable hardware
1 Department of Physics and Astronomy "Augusto Righi", Alma Mater Studiorum - University of Bologna, Viale Berti-Pichat 6/2, Bologna, Italy
2 INFN - National Institute for Nuclear Physics, Viale Berti-Pichat 6/2, Bologna, Italy
3 Department of Physics and Geology, Alma Mater Studiorum - University of Perugia, Via Alessandro Pascoli, Perugia, Italy
4 INFN - CNAF Bologna, Viale Berti-Pichat 6/2, Bologna, Italy
* e-mail: marco.lorusso11@unibo.it
Published online: 6 May 2024
Machine learning (ML) and deep learning (DL) techniques are increasingly influential in High Energy Physics, necessitating effective computing infrastructures and training opportunities for users and developers, particularly concerning programmable hardware like FPGAs. A gap exists in accessible ML/DL on FPGA tutorials catering to diverse hardware specifications. To bridge this gap, collaborative efforts by INFN-Bologna, the University of Bologna, and INFN-CNAF produced a pilot course using virtual machines, inhouse cloud platforms, and AWS instances, utilizing Docker containers for interactive exercises. Additionally, the Bond Machine software ecosystem, capable of generating FPGA-synthesizable computer architectures, is explored as a simplified approach for teaching FPGA programming.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
