DOAJ Open Access 2024

Scalable training on scalable infrastructures for programmable hardware

Lorusso Marco Bonacorsi Daniele Travaglini Riccardo Salomoni Davide Veronesi Paolo +5 lainnya

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Physics

Penulis (10)

Lorusso Marco

Bonacorsi Daniele

Travaglini Riccardo

Salomoni Davide

Veronesi Paolo

Michelotto Diego

Mariotti Mirko

Bianchini Giulio

Costantini Alessandro

Duma Doina Cristina

Format Sitasi

APA MLA BibTeX

Marco, L., Daniele, B., Riccardo, T., Davide, S., Paolo, V., Diego, M. et al. (2024). Scalable training on scalable infrastructures for programmable hardware. https://doi.org/10.1051/epjconf/202429508014

Akses Cepat

Lihat di Sumber doi.org/10.1051/epjconf/202429508014

Informasi Jurnal

Tahun Terbit: 2024
Sumber Database: DOAJ
DOI: 10.1051/epjconf/202429508014
Akses: Open Access ✓