Update README

README.md

@@ -1,6 +1,12 @@
 # Deeploy
 
-Deeploy is a python tool to generate low-level optimized C Code for multi-cluster, heterogeneous SoCs. Its goal is to enable configurable deployment flows from a bottom-up compiler perspective, modelling target hardware in a fine-grained and modular manner.
+![CI](https://github.com/pulp-platform/Deeploy/actions/workflows/CI.yml/badge.svg?branch=devel)
+![Docker](https://github.com/pulp-platform/Deeploy/actions/workflows/BuildDocker.yml/badge.svg)
+[![GitHub last commit](https://img.shields.io/github/last-commit/pulp-platform/Deeploy)](#)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+![](https://img.shields.io/badge/Provided_by_PULP_Platform-24AF4B)
+
+Deeploy is an ONNX-to-C compiler that generates low-level optimized C Code for multi-cluster, heterogeneous SoCs. Its goal is to enable configurable deployment flows from a bottom-up compiler perspective, modeling target hardware in a fine-grained and modular manner.
 
 Deeploy is developed as part of the PULP project, a joint effort between ETH Zurich and the University of Bologna.
 
@@ -10,12 +16,69 @@ Unless specified otherwise in the respective file headers, all code checked into
 
 ## Getting started
 
-To install Deeploy, simply run
+Installing Deeploy is as simple as running:
 ```
 pip install -e .
 ```
-to download and install the Python dependencies. Run
+However, to run the code generated by Deeploy on a certain target, you need the toolchains and the simulators associated with this platform.
+
+We provide a Docker container where Deeploy works Out-of-the-Box (*i.e.* with all the dependencies pre-installed). To pull the docker image, run:
+```
+docker pull ghcr.io/pulp-platform/deeploy:main
+```
+Then you can start the container in interactive mode with:
+```
+docker run -it ghcr.io/pulp-platform/deeploy:main
+```
+From the container, clone Deeploy, its submodules, and install the package with:
+```
+git clone https://github.com/pulp-platform/Deeploy.git && cd Deeploy
+git submodule update --init --recursive
+pip install -e .
+```
+Congratulations, you installed Deeploy and its dependencies! Now, to test your installation let's run one simple test on each platform with the following commands:
+```
+cd DeeployTest && source /app/install/pulp-sdk/configs/siracusa.sh
+python testRunner_generic.py -t Tests/Adder
+python testRunner_cortexm.py -t Tests/Adder
+python testRunner_mempool.py -t Tests/Adder
+python testRunner_siracusa.py -t Tests/Adder --cores=8
+```
+
+You can find the ONNX file in `DeeployTest/Tests/Adder`, to visualize it, you can use [Netron](https://netron.app/). You can also find the generated code for the platform X in `TEST_X` in `DeeployTest` and you should notice that the generated code for the `Adder` test is very simple. However, this gets more complex when you add tiling. Let's generate the code for a single layer but using tiling this time:
+```
+python testRunner_tiled_siracusa.py -t Tests/testMatMul --cores=8 --l1=16000
+``` 
+Now you can open the generated code in `DeeployTest/TEST_SIRACUSA/Tests/testMatMul/Network.c` and see how we executed a tiled layer.
+
+## Supported Platforms
+
+- **Generic CPU:**
+- **CortexM Processors:**
+    - Simulators: [QEMU](https://www.qemu.org/)
+- **MemPool extended with ITA:**
+    - Hardware: [Mempool paper](https://arxiv.org/abs/2303.17742), [ITA paper](https://arxiv.org/abs/2307.03493)
+    - Simulators: [Banshee](https://github.com/pulp-platform/banshee)
+- **Siracusa:**
+    - Hardware: [Siracusa paper](https://arxiv.org/abs/2312.14750)
+    - Simulators: [GVSOC](https://github.com/gvsoc/gvsoc)
+
+## Documentation
+
+To build the documentation, simply run:
 ```
 make docs
 ```
-and open `docs/_build/html/index.html` for more extensive documentation & getting started guides.
+Then open `docs/_build/html/index.html` for more extensive documentation & getting-started guides.
+
+## Publications
+
+### ESWEEK 2024: Deeploy: Enabling Energy-Efficient Deployment of Small Language Models On Heterogeneous Microcontrollers
+```
+@article{scherer2024deeploy,
+  title={Deeploy: Enabling Energy-Efficient Deployment of Small Language Models On Heterogeneous Microcontrollers},
+  author={Scherer, Moritz and Macan, Luka and Jung, Victor and Wiese, Philip and Bompani, Luca and Burrello, Alessio and Conti, Francesco and Benini, Luca},
+  journal={arXiv preprint arXiv:2408.04413},
+  year={2024}
+}
+```