Use MATLAB to Train on Jetson Nano

Setup

Overview

To train the AI using MATLAB, I will write a script on MATLAB and then use GPU Coder to flash the function/model on the JetRacer. So all the debugging process will be on MATLAB.

Key Concepts

• Deep Learning with MATLAB

1. The fundamental of training an AI system is called deep learning. MATLAB provides a variety of helpful toolboxes, pre-trained networks, and tools to process data into useful input for an AI [1].
2. The training can be stored as a model, usually in a .MAT format, which can be reused. Hence, we do not have to train a model every time we want to use it.
3. Data preprocessing—including image resizing, augmentation, and normalization—is handled with MATLAB functions such as imageDatastore and augmentedImageDatastore.

Tabelle 4: Deep Learning with MATLAB Setup

#		Description	Pictures
1	Data Processing	image resizing: augmentation: normalization:
2	Model training
3	Output

• Gpu Coder

1. GPU Coder generates optimized CUDA® C++ from your MATLAB® algorithms, unlocking the parallel compute power of the Jetson GPU for real-time processing on your JetRacer and improve 40 times the performance on the jetracer [2][3].
2. Helps target GPUs for automotive applications.

Tabelle 4: GPU Coder Setup

#		Description	Pictures
1	Access GPU APP	Matlab provides built-in app to generate Cuda compatible code.
2	Select Entry-Point Function	For CUDA code to be successfully generated, the MATLAB script must be a function. Hence, your script needs to be converted to a function.
3	Select Input types	A second file is required for the GPU Coder to Know what asre the inputs and Outputs
4	Select Targeted GPU	The CUDA code can be Generated directly on the chosen device or generated on the main pc and manually transported on the device
5	Generate c/c++

Result

generated files