Edge Inference of Image Super Resolution Deep Learning Models

Deployment of Deep learning Image Super-Resolution Models in Xilinx Zynq MPSoC ZCU102

Instructions

Follow the .pdf file in the repository for complete information

Prerequisites

• Knowledge on FPGA and Integrating IPs in Vivado
• Knowledge on Computer Architecture and Embedded Systems
• Linux Operating system - Petalinux/Yocto
• Basics of Deep learning
• Programming Languages: Cpp and Python
• Frameworks/libraries: Opencv,Numpy,Tensorflow

Tools

• Vitis v2019.2
• Vitis AI SDK v1.0
• Vivado v2019.2
• Petalinux v2019.2

Credits

• Francesco Cardinale for Image Super Resolution Models
• Morgan giraud for freeze_graph.py
• fengwang for subpixel layer

Citation

This is my Under Graduate Project done at Healthcare Technology Innovation Centre IIT Madras. Please Cite if it helps your work

@misc{gokulakrishnan2020ISRFPGA,
  title={Deploying Deep learning Image Super-Resolution Models in Xilinx Zynq MPSoC ZCU102},
  author={Gokula Krishnan Ravi},
  year={2020},
  howpublished={\url{https://github.com/gkrislara/Image-super-resolution-FPGA}},
}