This repository demonstrates ZigZag framework's capabilities and gives a tutorial of how to use it.
To reproduce these notebooks, all you need to have to do is the following:
git clone https://github.com/ZigZag-Project/zigzag-demo.git
cd zigzag-demo
conda create -n zigzag python=3.9
conda activate zigzag
pip install zigzag-dse
conda install -c anaconda ipykernel jupyter
python -m ipykernel install --user --name=zigzag
Now, ZigZag is installed and it's API is exposed through from zigzag import api
in your code. Also, the conda environment is exposed to Jupyter Notebook.
You can start your Jupyter Notebook:
jupyter notebook
Select the correct conda environment:
- Demo1: Run a DNN on an accelerator, get energy and latency breakdown.
- Demo2: Hardware architecture comparison.
- Tutorial1: The inputs and outputs of ZigZag - a single-layer example run.
- Tutorial1 advance: The inputs and outputs of ZigZag - a multi-layer example run, with different layer types and residual/branch connection.
- Tutorial2: Look into the ZigZag API function.