GUINNESS: A GUI based binarized Neural NEtwork SyntheSizer toward an FPGA (Trial version)

This GUI based framework includes both a training on a GPU, and a bitstream generation for an FPGA using the Xilinx Inc. SDSoC. This tool uses the Chainer deep learning framework to train a binarized CNN. Also, it uses optimization techniques for an FPGA implementation. Details are shown in following papers:

[Nakahara IPDPSW2017] H. Yonekawa and H. Nakahara, "On-Chip Memory Based Binarized Convolutional Deep Neural Network Applying Batch Normalization Free Technique on an FPGA," IPDPS Workshops, 2017, pp. 98-105.

[Nakahara FPL2017] H. Nakahara et al., "A Fully Connected Layer Elimination for a Binarized Convolutional Neural Network on an FPGA", FPL, 2017, pp. 1-4.

[Nakahara FPL2017 Demo] H. Nakahara et al., "A demonstration of the GUINNESS: A GUI based neural NEtwork SyntheSizer for an FPGA", FPL, 2017, page 1.

1. Requirements:

Ubuntu 16.04 LTS (14.04 LTS is also supported)

Python 3.5.1 (Note that, my recommendation is to install by Anaconda 4.1.0 (64bit)+Pyenv, for Japanese Only, I prepared the Python 3.5 by following http://blog.algolab.jp/post/2016/08/21/pyenv-anaconda-ubuntu/)

CUDA 8.0 (+GPU), CuDNN 6.0 (Also, you must sign up the NVidia developer account)

Chainer 1.24.0 + CuPy 2.0

Xilinx Inc. SDSoC 2017.4

FPGA board: Xilinx ZC702, ZC706, ZCU102, Digilent Zedboard, Zybo
(Soon, I will support Intel's FPGAs!, and the PYNQ board)

PyQt4, matplotlib, OpenCV3, numpy, scipy, (Above libraries are installed by the Anaconda, however, you must individually install the OpenCV by "conda install -y -c menpo opencv3")

2. Setup Libraries

Install the following python libraries:

Chainer

sudo pip install chainer==1.24.0

PyQt4 (not PyQt5!), it is already installed by the Anaconda

sudo apt-get install python-qt4 pyqt4-dev-tools

OpenCV3

conda install -y -c menpo opencv3

3. Run GUINNESS

$ python guinness.py

4. Tutorial

Read a following document (25/Oct./2017 Updated!!)

1 The GUINNESS introduction and BCNN implementation on an FPGA
guinness_tutorial1_v2.pdf https://www.dropbox.com/s/oe6gptgyi4y92el/guinness_tutorial1_v2.pdf?dl=0

2 The GUINNESS for the Intel FPGAs (Soon, will be uploaded)

3 Pedestrian detection (Under preparing)

4 Make a custom IP core for your own FPGA board (Under preparing)

5. On-going works

This is a just trial version. I have already developed the extend version including following ones.

Supporing the Intel's FPGA (DE5-net, DE10-nano, and DE5a-net boards with the Intel SDK for OpenCL)

High performance image recognition (fully pipelined and SIMD CNNs)

Object detector on a low-cost FPGA (e.g., pedestrian detection)

FPGA YOLOv2 (ZCU102 board)

Pedestrian Detector (Zedboard)

If you are interesting the extended one, please, contact me.

6. Acknowledgements

This work is based on following projects:

Chainer binarized neural network by Daisuke Okanohara
https://github.com/hillbig/binary_net

Various CNN models including Deep Residual Networks (ResNet)
for CIFAR10 with Chainer by mitmul
https://github.com/mitmul/chainer-cifar10

This research is supported in part by the Grants in Aid for Scientistic Research of JSPS,
and an Accelerated Innovation Research Initiative Turning Top Science and Ideas into High-Impact
Values program(ACCEL) of JST. Also, thanks to the Xilinx University Program (XUP), Intel University Program, and the NVidia Corp.'s support.