End-to-end example from training a CNN to getting the VHDL toplevel wrapper

Workflow

Set the pythonpath: export PYTHONPATH=/path/to/pocket-cnn/code/python_tools
Choose a framework of your choice to train the CNN. In this example, Pytorch is used, because it has built in ONNX export. A list of available frameworks can be found at https://github.com/onnx/tutorials.
Install the python modules numpy, onnx and fpbinary.
Clone the repository: git clone git@github.com:marph91/pocket-cnn.git
Run the script: cd pocket-cnn/examples/end_to_end && ./end_to_end.sh The script executes the following steps:
- Train a CNN and save it in ONNX format. The model is an adapted Lenet, which is trained on the MNIST dataset. Example training results are an accuracy of 91% and a loss of 0.3253 on the test images.
- Quantize the exported ONNX model.
- Create a synthesizable toplevel wrapper and corresponding weights files from the ONNX model.
Import the generated VHDL toplevel file (examples/end_to_end/work/top_wrapper.vhd) in the project, like the other sources from code/vhdl/src. Make sure the weights path is set correctly and the weights files don't get moved later.
Synthesize with a tool of your choice. Right now it was only tested with Xilinx Vivado.