Tutorials

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.

Basics

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_via_QONNX
- 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.

Advanced

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.
3_folding
- Describes the use of FINN parallelization parameters (PE & SIMD), also called folding factors, to efficiently optimize models so as to extract the maximum performance out of them.
4_advanced_builder_settings
- Provides a more detailed look into the FINN builder tool and explores different options to customize your FINN design.

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.