PROJECT NAME:

PYNQ Classification - Python on Zynq FPGA for Convolutional Neural Networks (Alpha Release)

BRIEF DESCRIPTION:

This repository presents a fast prototyping framework, which is an Open Source framework designed to enable fast deployment of embedded Convolutional Neural Network (CNN) applications on PYNQ platforms.

CITATION:

If you make use of this code, please acknowledge us by citing our article:

@inproceedings{Wang_FCCM18,
    author={E. Wang and J. J. Davis and P. Y. K. Cheung},
    booktitle={IEEE Symposium on Field-programmable Custom Computing Machines (FCCM)},
    title={{A PYNQ-based Framework for Rapid CNN Prototyping}},
    year={2018}
}

INSTRUCTIONS TO BUILD AND TEST THE PROJECT:

Repository Organisation

The project demo accepts pre-trained CNN models in either Caffe or Theano syntax, hence the step 1 and 2 introduces how to install Caffe and Theano (with Lasagne) on PYNQ. Step 3 explains how to download and run the demos for LeNet and CIFAR-10 (Caffe "quick" version) models.

For a quick overview on the project please watch my video tutorial.

Vivado Project Setup - How to implement more CNN models?

Please go to hw/README.md for guide on regenerating the Vivado and Vivado HLS projects.

References

1. BNN-PYNQ

If you find BNN-PYNQ useful, please cite the FINN paper:

@inproceedings{finn,
author = {Umuroglu, Yaman and Fraser, Nicholas J. and Gambardella, Giulio and Blott, Michaela and Leong, Philip and Jahre, Magnus and Vissers, Kees},
title = {FINN: A Framework for Fast, Scalable Binarized Neural Network Inference},
booktitle = {Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays},
series = {FPGA '17},
year = {2017},
pages = {65--74},
publisher = {ACM}
}

2. Caffe

Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}

3. Theano

@ARTICLE{2016arXiv160502688short,
   author = {{Theano Development Team}},
    title = "{Theano: A {Python} framework for fast computation of mathematical expressions}",
  journal = {arXiv e-prints},
   volume = {abs/1605.02688},
 primaryClass = "cs.SC",
 keywords = {Computer Science - Symbolic Computation, Computer Science - Learning, Computer Science - Mathematical Software},
     year = 2016,
    month = may,
      url = {http://arxiv.org/abs/1605.02688},
}

4. Lasagne

@misc{lasagne,
  author       = {Sander Dieleman and
                  Jan Schlüter and
                  Colin Raffel and
                  Eben Olson and
                  Søren Kaae Sønderby and
                  Daniel Nouri and
                  others},
  title        = {Lasagne: First release.},
  month        = aug,
  year         = 2015,
  doi          = {10.5281/zenodo.27878},
  url          = {http://dx.doi.org/10.5281/zenodo.27878}
}