FINN provides several Jupyter notebooks that can help to get familiar with the basics, the internals and the end-to-end flow in FINN. All Jupyter notebooks can be found in the repo in the notebook folder.


The notebooks in this folder should give a basic insight into FINN, how to get started and the basic concepts.

  • 0_how_to_work_with_onnx
    • This notebook can help you to learn how to create and manipulate a simple ONNX model, also by using FINN
  • 1_brevitas_network_import
    • This notebook shows how to import a Brevitas network and prepare it for the FINN flow.

End-to-End Flow

There are two groups of notebooks currently available under the end2end_example directory :

  • cybersecurity shows how to train a quantized MLP with Brevitas and deploy it with FINN using the Command Line Entry build system.
  • bnn-pynq shows the internal compiler steps that take pretrained Brevitas QNNs on MNIST and CIFAR-10 and generate the FPGA accelerator.


The notebooks in this folder are more developer oriented. They should help you to get familiar with the principles in FINN and how to add new content regarding these concepts.

  • 0_custom_analysis_pass
    • Explains what an analysis pass is and how to write one for FINN.
  • 1_custom_transformation_pass
    • Explains what a transformation pass is and how to write one for FINN.
  • 2_custom_op
    • Explains the basics of FINN custom ops and how to define a new one.

FINN Example FPGA Flow Using MNIST Numerals

Next to the Jupyter notebooks above there is a tutorial about the command-line build_dataflow here which shows how to bring a FINN compiled model into the Vivado FPGA design environment.