PYNQ|FPGA Projects

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It is about FPGA Projects using Python Programming for deep learning ,Computer vision applications and Image processing, EDK & SDK development using Vivado. Below FPGA Projects are developed using PYNQ-Z1 and PYNQ-Z2

Real-time edge detection using Python in FPGA – PYNQ

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Published in:  ARCS Workshop 2019; 32nd International Conference on Architecture of Computing Systems

Real-time edge detection using FPGA
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Edge detection of an image is done in FPGA boards, now well popular AI-based applications are using Computer vision for image-related applications. This Computer Vision is now possible in FPGA through Python compatibility in PYNQ boards


Object recognition using Tiny YOLO using Python in PYNQ-Z2

In this Object recognition technique, Tiny Yolo Model is used, which is the pre-trained model trained to recognize multiple objects. The below figure is the Architecture of Tiny Yolo.


Real-time moving object detection using Python in FPGA | PYNQ

In this project, PYNQ Z2 board is used, the python program is developed using Jupyter Notebook by connecting PYNQ with the network through Ethernet to get the IP Address of the board. So that Jupyter Notebook can be opened using that IP Address. Real-time moving object detection application is developed using Computer Vision using Python.


Real-time Color detection in FPGA using Python in FPGA | PYNQ

In this project, the PYNQ Z2 board is used, the python program is developed using Jupyter Notebook. Jupyter notebook is opened by making connections with 1st Method: Connecting Ethernet cable to the PYNQ Board and to PC directly and by assigning static IP addresses for the PYNQ board. With the device IP Address, Jupyter notebook will be opened. A real-time Color detection application is developed using Computer Vision with Python.


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