Description

Introduction

Now FPGA is compatible to run the Python program. PYNQ FPGA is here to perform Computer Vision, Deep learning applications. PYNQ booting image is provided by Xilinx, it should be booted to the SD Card, makes PYNQ Z2 to open Jupyter Notebook by connecting the board with the Network.

Abstract

CNN is a popular Neural Network for image classification, through the Jupyter notebook. By using Python programming, Deep learning program is developed and run in PYNQ Z2, which uses the FPGA computational power to load Neural Network to classify the object present in an image.

Existing System

In the existing system, programming FPGA for simple image-based applications lead to lines of codes with very less efficiency.

Proposed System

In this proposed system, FPGA is used for deep learning application using PYNQ boards. In this project, CNN based image classification is done using Deep learning.

Connection description

PYNQ Z2 is connected with the router through Ethernet cable. Booted SD Card is inserted to the PYNQ board to make it support Python programming through Jupyter Notebook. USB Camera is connected to the USB port of the PYNQ board.

Project description

In this project, you will learn to program FPGA with python to classify the object present in an image by drawing the bounding box for every image with its labels and accuracy of the detected object. To classify image CNN is used, here you can see the computational power of PYNQ Z2 while performing Deep learning in Jupyter Notebook.

Hardware required

• PYNQ Z2
• Router
• Laptop/PC
• USB Camera

Software required

• PYNQ Z2 boot image
• SD Card Formatter
• Etcher/Win32 disk imager

Result

This project classifies the object present in an image using CNN, that the resulting image can be seen in the Jupyter Notebook. Every program files and result and network model is stored in SD Card, it can be retrieved from the jupyter notebook like datasets.