• Introduction
• Requirements
• Usage
• FAQ and Issues
• Acknowledgement
• Citation

This project is aimed at deploying accurate, unbiased neural networks, which can be used in hospitals to detect arrhythmia at early stages, with processing on low-power, cheap and edge hardware to preserve privacy of patients.

Neural Net Architecture

• An ubuntu PC or VM(8GB RAM minimum) with sudo access (Tested on Ubuntu 20.04 Focal Fossa)
• A PYNQ supported board connected to the internet via ethernet (Tested on PYNQ-Z2)
• SD Card (16GB minimum)
• Ethernet cable
• Micro USB cable

Note: For Installing and setting up environment refer to SETUP.md

cd /path/to/finn
./run-docker.sh notebook
cd notebooks
git clone [email protected]:Adityasrinivas24/ecg-classification-pynq.git

Install required packages outside docker environment

pip3 install -r requirements.txt

Pretrained Models

Outside the Docker Environment
1-ecgnet-train-test trains and validates a quantized neural network to classify ecg images

In the Docker Environment
2-ecgnet-onnx-export exports the model to finn compatible onnx format
3-ecgnet-finn-compile has all the steps required to compile design using finn

On PYNQ
4-ecgnet-pynq-deploy contains details on pynq deployment and results

Resolve the NewConnectionError raised by pip during library installations in PYNQ refer this discussion

Other FAQs and Issues can be found here

@software{brevitas,
  author       = {Alessandro Pappalardo},
  title        = {Xilinx/brevitas},
  year         = {2022},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.3333552},
  url          = {https://doi.org/10.5281/zenodo.3333552}
}

@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}
}