LSTM-PYNQ Pip Installable Package

This repo contains the pip install package for Quantized LSTM on PYNQ. Currently one overlay is included, that performs Optical Character Recognition (OCR) of Fraktur text.

If you find it useful, we would appreciate a citation to:

Hardware Architecture of Bidirectional Long Short-Term Memory Neural Network for Optical Character Recognition, V. Rybalkin, M. R. Yousefi, N. Wehn, and D. Stricker, In Proceedings of the Conference on Design, Automation & Test in Europe (DATE). European Design and Automation Association, 2017, pp. 1390–1395, DOI

BibTeX:

@inproceedings{rybalkin2017hardware,
  title={Hardware architecture of bidirectional long short-term memory neural network for optical character recognition},
  author={Rybalkin, Vladimir and Wehn, Norbert and Yousefi, Mohammad Reza and Stricker, Didier},
  booktitle={Proceedings of the Conference on Design, Automation \& Test in Europe},
  pages={1394--1399},
  year={2017},
  organization={European Design and Automation Association}
}

This design became a winner of Xilinx Open Hardware Design Contest 2016 in PhD Embedded Category.

This repo is a joint release of University of Kaiserslautern, Microelectronic Systems Design Research Group: Vladimir Rybalkin, Muhammad Mohsin Ghaffar, Norbert Wehn in cooperation with Xilinx, Inc.: Alessandro Pappalardo, Giulio Gambardella, Michael Gross, Michaela Blott.

Quick Start

In order to install it to your PYNQ (on PYNQ v2.0), connect to the board, open a terminal and type:

sudo pip3.6 install git+https://github.com/tukl-msd/LSTM-PYNQ.git 

This will install the LSTM-PYNQ package to your board, and create a lstm directory in the Jupyter home area. You will find the Jupyter notebooks to test the LSTM in this directory.

Repo organization

The repo is organized as follows:

• lstm: contains the pip installed package.
  • lstm.py: contains the PynqLSTM abstract class description.
  • ocr.py: contains the PynqOCR abstract class and PynqFrakturOCR class descriptions.
  • bitstreams: bitstream for the Fraktur OCR overlay.
  • libraries: pre-compiled shared objects for low-level driver of the overlays.
  • datasets: contains support files for working with a given dataset.
• notebooks: lists a set of python notebooks examples, that during installation will be moved in /home/xilinx/jupyter_notebooks/lstm/ folder.