Deep Learning Toolbox™ (required for deep learning).Here is a list of required products for this example. GPU Coder helps convert MATLAB code into CUDA code for the NVIDIA Jetson. Setup the Jetson and the Host Computerįirst, let’s look at some setup steps needed on the MATLAB Host and Jetson. While this workflow is for the NVIDIA Jetson TX2, the same can be applied to other NVIDIA embedded products as well. NVIDIA Jetson is a power efficient System-on-Module (SOM) with CPU, GPU, PMIC, DRAM, and flash storage for edge AI applications that comes in a variety of configuration specifications.
Let’s dive into using MATLAB to deploy this network to an NVIDIA Jetson. Remember: whether it’s life or autonomous systems, everything is a compromise ?. See our trained network identifying buoys and a navigation gate in a test dataset.īut what next? When building an autonomous, untethered system like an AUV for RoboSub, the challenge is transferring these algorithms from a desktop/development environment onto an embedded computer, characterized by a low power requirement, but also lower memory and compute capabilities. We trained a YOLOv2 network to identify different competition elements from RoboSub–an autonomous underwater vehicle (AUV) competition. Our previous blog post, walked us through using MATLAB to label data, and design deep neural networks, as well as importing third-party pre-trained networks.
