In the past few years, using Machine and Deep Learning techniques has become more and more viable, thanks to the availability of tools which make the need of specific knowledge in the realm of data science and complex networks less vital to achieve a satisfactory final result in a variety of research fields. This process has caused an explosion in the adoption of such techniques, e.g. in the context of High Energy Physics.
The range of applications for ML becomes even larger if we consider the implementation of these algorithms on low-latency hardware like FPGAs which promise smaller latency with respect to traditional inference algorithms running on general purpose CPUs.

This paper presents and discusses the activity running at the University of Bologna and INFN-Bologna where a new open-source project from Xilinx called PYNQ is being tested. Its purpose is to grant designers the possibility to exploit the benefits of programmable logic and microprocessors using the Python language and libraries. This new software environment can be deployed on a variety of Xilinx platforms, from the simplest ones like ZYNQ boards, to more advanced and high performance ones, like Alveo accelerator cards and AWS EC2 F1 instances.

The use of cloud computing in this work lets us test the capabilities of this new workflow, from the creation and training of a Neural Network and the creation of a HLS project using HLS4ML, to testing the predictions of the NN using PYNQ APIs and functions written in Python