NuEdgeWise

The Tiny ML Tool provides a platform for training and deployment using TensorFlow Lite on Nuvoton's MCU/MPU.

• The NuEdgeWise tools offer Jupyter Notebooks with a user-friendly interface, simplifying the process of working with Tiny ML.
• Please follow second & third steps to install the Python environment once and explore all the ML tools/examples provided below.
• We utilize TensorFlow Lite for Microcontrollers as the inference framework for Nuvoton MCU, and currently, the NuEdgeWise tools provide TensorFlow Lite (TFLite) and TFLite Vela models.
• Regarding the PyTorch model, please review the fifth step to assist you in converting the PyTorch model to a (TFLite) model.

1. Tool Table

Tool	Use Case	Model	M55M1	M467	MA35D1	Description
ML_KWS	Keyword Spotting	DNN/DS-CNN	✔️	✔️	🔹	Keyword spotting with a small vocabulary (<=1s).
ML_G-Sensor	Gesture Recognition Magic Wand	CNN	🔹	✔️	🔹	The data consists of 3-dimensional accelerometer readings captured during various gestures. In this Tool, we provide functionality for data collection.
ML_Image_Classification	Image Classification	MobileNet/efficientnet/fdmobilenet/shufflenet	✔️	🔹 (shufflenet)	🔹	We utilize transfer learning and fine-tuning techniques, where the pre-trained model is MobileNet trained on the ImageNet dataset. Users have the flexibility to train the model further with their own data.
ML_Object_Detection	Object Detection	SSD_MobileNet_fpnlite v2/v3	✔️	❌	✔️	We utilize the TensorFlow Object Detection API, which supports various models. For our MPU's edge use-case, we opt for a smaller model. If users wish to experiment with SSD_MobileNet_fpnlite_v3, please use the TF1 environment. More details regarding the TF1 environment can be found in the provided link.
ML_YOLO	Object Detection	Yolo-fastest v1	✔️	❌	✔️	We use DarkNet training with a highly compact YOLO model. This tool provides features for converting the model to TensorFlow Lite format and optimizing it using Vela.
ML_Gearbox_Fault_Diagnosis	Anomaly Detection	DNN/Autoencoder	🔹	✔️	🔹	A basic practice for Tiny ML includes training a model, converting it to TFLite format, and deploying it on an EVK.
ML_VWW	Visual Wake Words	Small MobileNet RGB/gray	✔️	✔️	🔹	In the microcontroller vision use-case, the objective is to identify whether a person (or any other object of interest) is present in an image.

• ✔️ : The model is ready to run on the device, and we provide example board inference code.
• 🔹 : The model is ready to run on the device, and users need to develop their own inference code.
• ❌ : The model cannot currently run on the device.

2. Installation & Env Create

A. Create a Python Environment

• If you are already familiar with Python and virtual environments, you can skip this step. Please be reminded that NuEdgeWise uses Python 3.8.
• We recommand to use Miniforge, and please download the Miniforge3 basing on your OS.
• Execute the installation steps for Miniforge3.exe.

B. Create NuEdgeWise Virtual Environment

• (A.) Open miniforge.
• (B.) Execute conda create --name NuEdgeWise_env python=3.8.13 to create new python environment.
• (C.) Execute conda activate NuEdgeWise_env to open NuEdgeWise_env environment.
• (D.) Go to this NuEdgeWise folder(From git clone or download it directly) and Execute python -m pip install -r requirements.txt.
• For Windows users, we provide a batch file to execute these commands all at once.
• Almost all the required Python packages are already installed in this Conda environment. However, for ML_Object_Detection, additional installation steps are required. It is recommended to follow the installation steps provided in the ML_Object_Detection repository.

C. Alternatively, Docker CPU version

• (A.) Download ML_KWS, ML_Image_Classification same location as NuEdgeWise/
• (B.) In NuEdgeWise/
• (C.) Build image: docker build -t nuedgewise:v1 .
• (D.) Run container: docker compose run nuedgewise

3. Choose your use case/application

• Download the directory from the table above and open Miniforge or your python environment, selecting the NuEdgeWise environment.
• Please refer to the readme in the Tools section for instructions on how to use it.
• Now you can start running the Tiny-ML examples from the Jupyter notebook in each Tools.
• In each tool/use-case, we also provide example inference code for Nuvoton MCU/MPU devices.

4. Description

• Fig1: The general workflow of our tiny ML tools.

flowchart LR
  subgraph Data
      direction LR
      A[("User or open source dataset (*2)")] -->B(Data Prepare)
  end
   subgraph Train_Model
      direction LR
      E{If acceptable?} -->|No|C(Train & Model Creating)
      D(Evaluation & Test) --> E
      C-->D 
  end
  subgraph Deployment
      direction LR
      F(Convert)-->G[Target Device: MCU/MPU]
  end
 
  Data~~~Train_Model~~~Deployment 
  B-->C
  E-->|Yes|F

Loading

• All of these tools can be used to train with custom datasets and convert them to deployment-ready formats such as TFLite or TFLite for Microcontrollers.
• (*2) ML_KWS and ML_G-Sensor are able to collect data by Nuvoton EVK board.
• ML_Image_Classification, ML_VWW and ML_YOLO also support the Vela compiler for MCU+NPU use-cases. Other tools/models can also be applied to Vela using the ML_YOLO vela/ directory as a reference.

5. PyTorch to Tflite

• The converting step is PyTorch => ONNX => Tflite.
• (A.) PyTorch provides a built-in feature for exporting the model to ONNX format: https://pytorch.org/tutorials/beginner/onnx/export_simple_model_to_onnx_tutorial.html
• (B.) To convert ONNX to TFLite, please refer to PINTO0309/onnx2tf. Follow the installation step provided, and for Nuvoton products, you may utilize the following commands exclusively

# INT8 Quantization, Full INT8 Quantization
# INT8 Quantization with INT16 activation, Full INT8 Quantization with INT16 activation,
# Dynamic Range Quantization

# INT8 Quantization (per-channel)
onnx2tf -i emotion-ferplus-8.onnx -oiqt
# INT8 Quantization (per-tensor)
onnx2tf -i emotion-ferplus-8.onnx -oiqt -qt per-tensor