Deep Learning algorithms are gaining momentum as main components in a large number of fields, from computer vision and robotics to finance and biotechnology. At the same time, the use of Field Programmable Gate Arrays (FPGAS) for data-intensive applications is increasingly widespread thanks to the possibility to customize hardware accelerators and achieve high-performance implementations with low energy consumption. Moreover, FPGAS have demonstrated to be a viable alternative to GPUS in embedded systems applications, where the benefits of the reconfigurability properties make the system more robust, capable to face the system failures and to respect the constraints of the embedded devices. In this work, we present a framework to efficiently implement Deep Learning algorithms by exploiting the PYNQ platform, recently released by Xilinx. The case study application is tested on PYNQ-Z1 board, commonly used in embedded system applications.

On how to efficiently implement deep learning algorithms on PYNQ platform

Stornaiuolo, Luca;Santambrogio, Marco;Sciuto, Donatella
2018-01-01

Abstract

Deep Learning algorithms are gaining momentum as main components in a large number of fields, from computer vision and robotics to finance and biotechnology. At the same time, the use of Field Programmable Gate Arrays (FPGAS) for data-intensive applications is increasingly widespread thanks to the possibility to customize hardware accelerators and achieve high-performance implementations with low energy consumption. Moreover, FPGAS have demonstrated to be a viable alternative to GPUS in embedded systems applications, where the benefits of the reconfigurability properties make the system more robust, capable to face the system failures and to respect the constraints of the embedded devices. In this work, we present a framework to efficiently implement Deep Learning algorithms by exploiting the PYNQ platform, recently released by Xilinx. The case study application is tested on PYNQ-Z1 board, commonly used in embedded system applications.
2018
Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
9781538670996
Deep Learning; DNN; Failure recovery; FPGA; PYNQ; Hardware and Architecture; Control and Systems Engineering; Electrical and Electronic Engineering
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1063112
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 24
  • ???jsp.display-item.citation.isi??? 16
social impact