%0 Thesis
%A Sun, Wenbo
%T Deployment of neural networks through PYNQ
%D 2023
%U https://hdl.handle.net/20.500.14352/88205
%X The PYNQ platform provides a python interface for accessing FPGA resources, which gives us the opportunity to efficiently deploy neural network models on FPGA to achieve high-performance and real-time image classification and target detection tasks. This hardware-accelerated approach can provide faster inference speed and lower power consumption than software-accelerated approach.In this research and development project, our main research objective is to deploy neural networks on PYNQ. I have used the PYNQ-Z1 development board for experiments. Four type of networks have been deployed, namely: a YOLO network, a BNN network, a ResNet network and a MobileNetv2 network. After deployment, I have compared their accuracy and measured their execution time on hardware, achieving promising results for a resource-constrained device as the Z1 board.
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