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README_SBC.md

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Get Started with a single-board computer (SBC)

In the next window the project name, the output path and the neural network to be converted are passed.

In the next window you can select the target you want to execute the TensorFlow model. You can choose if you want to execute it on a MCU, an FPGA or an SBC. Here you select the SBCs button.

In the fourth window, the optimization algorithm pruning can be selected. It is important to know that only for fully connected and convolutional layers the pruning algorithm can be applied. For SBCs no quantization can be performed. SBCs have FPUs, so there is no advantage to quantizing the weights and input data.

If you select pruning, you can choose between two options:

  • Factor: For the fully connected and convolutional layers, a factor is specified in each case, which indicates the percentage of neurons or filters to be deleted from the layer.

  • Accuracy: The minimum accuracy of the neural network or the loss of accuracy that may result from pruning can be specified here.

The next window appears only if the pruning algorithm has been selected. In this window the training data are selected which the neural network requires for the optimization. The data can be transferred in different ways:

  • Path: images are to be used as training data. In the given path there are subfolders containing the name of the different classes of the neural network and the corresponding images.
  • File (CSV file): The data is stored in a CSV file. The rows of the CSV file contain the different data samples. The first or last column of the file have to contain the target label. The other columns contain the data of each sample.
  • File (Python file): The data is loaded and returned in a Python script. Here it is important that the Python script contains the function get_data() with the return values x_train, y_train, x_test, y_test (training data, training label, test data, test label). The return values here are NumPy arrays. An example of how such a file can look like can be found here.

If a CSV file is selected the CSV dataloader window opens. With the browse button a new CSV file can be selected again. By using the different separators, it is possible to define how the data is separated. The Preview button shows an overview of how the data will look with the selected settings. In addition, the label of each data series must be specified. Here it is possible to set the label to the first or the last position of a data series. In addition, the number of rows and columns in the data set is displayed. If all settings are correct, the settings can be taken over for the later optimization via the button Load data.

At the end, an overview of all selected parameters is displayed here. The button in the lower right corner starts the process of the conversion.

Model execution

The optimized model which has been converted to the TensorFlow Lite format is stored in the project folder which was specified as the output path. The following shows some code snippets that are needed to execute the TensorFlow Lite model on an SBC:

# Read the data of your TFLite model file
with open(tflite_model_file, 'rb') as f:
    tflite_model = f.read()
...
# Load TFLite model and allocate tensors
interpreter = tf.lite.Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
...
# Get input and output of model
input = interpreter.get_input_details()[0]
output = interpreter.get_output_details()[0]
...
# Set input data
interpreter.set_tensor(input['index'], input_data)
...
# Run the model
interpreter.invoke()
...
# Get model output
prediction = interpreter.get_tensor(output['index'])

Moreover, an example script for executing a TFLite model, trained on the MNIST dataset, can be found here.