finn-examples 0.0.5

FINN Examples on PYNQ for Zynq and Alveo

This repository contains a variety of customized FPGA neural network accelerator examples built using the FINN compiler, which targets few-bit quantized neural networks with emphasis on generating dataflow-style architectures customized for each network.

The examples here come with pre-built bitfiles, PYNQ Python drivers and Jupyter notebooks to get started, and you can rebuild them from source. Both PYNQ on Zynq and Alveo are supported.

Need help with a problem in this repo, or got a question? Feel free to ask for help in the GitHub discussions. In the past, we also had a Gitter channel. Please be aware that this is no longer maintained by us but can still be used to search for questions previous users had.

Quickstart

For Alveo we recommend setting up everything inside a virtualenv as described here. For PYNQ boards, all commands below must be prefixed with sudo or by first going into sudo su. We recommend PYNQ version 2.6.1 as some installation issues have been reported for PYNQ version 2.7.

First, ensure that your pip and setuptools installations are up-to-date on your PYNQ board or Alveo server:

python3 -m pip install --upgrade pip setuptools

Install the finn-examples package using pip:

# remove previous versions with: pip3 uninstall finn-examples
pip3 install finn-examples
# to install particular git branch:
# pip3 install git+https://github.com/Xilinx/finn-examples.git@dev

Retrieve the example Jupyter notebooks using the PYNQ get-notebooks command:

# on PYNQ boards, first cd /home/xilinx/jupyter_notebooks
pynq get-notebooks --from-package finn-examples -p . --force

You can now navigate the provided Jupyter notebook examples, or just use the provided accelerators as part of your own Python program:

from finn_examples import models
import numpy as np

# instantiate the accelerator
accel = models.cnv_w2a2_cifar10()
# generate an empty numpy array to use as input
dummy_in = np.empty(accel.ishape_normal, dtype=np.uint8)
# perform inference and get output
dummy_out = accel.execute(dummy_in)

Example Neural Network Accelerators

Dataset	Topology	Quantization	Supported boards	Supported build flows
CIFAR-10	CNV (VGG-11-like)	several variants: 1/2-bit weights/activations	all	Pynq-Z1 ZCU104 Ultra96
MNIST	3-layer fully-connected	several variants: 1/2-bit weights/activations	all	Pynq-Z1 ZCU104 Ultra96
ImageNet	MobileNet-v1	4-bit weights and activations 8-bit first layer weights	Alveo U250 ZCU104	ZCU104
ImageNet	ResNet-50	1-bit weights 2-bit activations 4-bit residuals 8-bit first/last layer weights	Alveo U250	-
RadioML 2018	1D CNN (VGG10)	4-bit weights and activations	ZCU104	ZCU104
MaskedFace-Net	BinaryCoP Contributed by TU Munich+BMW	1-bit weights and activations	Pynq-Z1	Pynq-Z1
Google Speech Commands v2	3-layer fully-connected	3-bit weights and activations	Pynq-Z1	Pynq-Z1

*Please note that for the non-supported Alveo build flows, you can use the pre-built FPGA bitfiles generated with older versions of the Vitis/Vivado tools. These bitfiles target the following Alveo U250 platform: xilinx_u250_xdma_201830_2.

We welcome community contributions to add more examples to this repo!

Supported Boards

Note that the larger NNs are only available on Alveo or selected Zynq boards.

finn-examples provides pre-built FPGA bitfiles for the following boards:

• Edge: Pynq-Z1, Pynq-Z2, Ultra96 and ZCU104
• Datacenter: Alveo U250

It's possible to generate Vivado IP for the provided examples to target any modern Xilinx FPGA of sufficient size. In this case you'll have to manually integrate the generated IP into your design using Vivado IPI. You can read more about this here.

Rebuilding the bitfiles

All of the examples here are built using the FINN compiler, and can be re-built or customized. See the build/README.md for more details.