readme

README.md

@@ -2,12 +2,10 @@
 
 ![Prototype](https://github.com/nicolasvalenchon/Portiloop/blob/main/images/photo_portiloop.jpg)
 
-Your training curves can be visualized in the Portiloop [wandb project](https://wandb.ai/portiloop).
-
 ## Quick start guide
 
 - clone the repo
-- cd to the root of the repo where `setup.py` is
+- cd to the root of the repo (i.e., the folder where `setup.py` is)
 - pip install with the -e option:
 ```terminal
 pip install -e .
@@ -16,10 +14,15 @@ pip install -e .
 - unzip the `datasets.zip` file and paste its content under `Portiloop>Software>dataset`
 - unzip the `experiments.zip` file and paste its content under `Portiloop>Software>experiments`
 
-### Inference / Portiloop simulation:
-The `simulate_Portiloop_1_input_classification.ipynb` [notebook](https://github.com/nicolasvalenchon/Portiloop/blob/main/notebooks/simulate_Portiloop_1_input_classification.ipynb) enables stimulating the Portiloop system with and perform inference.
+### Offline inference / simulation:
+The `simulate_Portiloop_1_input_classification.ipynb` [notebook](https://github.com/nicolasvalenchon/Portiloop/blob/main/notebooks/simulate_Portiloop_1_input_classification.ipynb) enables stimulating the Portiloop system and perform inference.
 This notebook can be executed with `jupyter notebook`.
 
 ### Training:
-We provide the bash scripts examples for `slurm` to train the model on HPC systems.
+Functions used for training are defined in python under the `Software` folder.
+We provide bash scripts examples for `SLURM` to train the model on HPC systems.
 Adapt these scripts to your configuration.
+Your training curves can be visualized in real time easily using [wandb](https://wandb.ai/portiloop) (the code is ready, you may adapt it to your project name and entity).
+
+### Hardware implementation:
+The current hardware implementation (pynq FPGA with Vivado / Vivado HLS) is provided under the `Hardware` folder.