Bookmark and Share

"Deep Learning and Vision Algorithm Development in MATLAB Targeting Embedded GPUs," a Presentation from MathWorks

Register or sign in to access the Embedded Vision Academy's free technical training content.

The training materials provided by the Embedded Vision Academy are offered free of charge to everyone. All we ask in return is that you register, and tell us a little about yourself so that we can understand a bit about our audience. As detailed in our Privacy Policy, we will not share your registration information, nor contact you, except with your consent.

Registration is free and takes less than one minute. Click here to register, and get full access to the Embedded Vision Academy's unique technical training content.

If you've already registered, click here to sign in.

See a sample of this page's content below:

Avinash Nehemiah, Product Marketing Manager for Computer Vision, and Girish Venkataramani, Product Development Manager, both of MathWorks, presents the "Deep Learning and Vision Algorithm Development in MATLAB Targeting Embedded GPUs" tutorial at the May 2017 Embedded Vision Summit.

In this presentation, you'll learn how to adopt a MATLAB-centric workflow to design, verify and deploy your computer vision and deep learning applications onto embedded NVIDIA Tegra-based platforms including Jetson TK1/TX1 and DrivePX boards. The workflow starts with algorithm design in MATLAB, which enjoys universal appeal among engineers and scientists because of its expressive power and ease-of-use. The algorithm may employ deep learning networks augmented with traditional computer vision techniques and can be tested and verified within MATLAB.

Next, a compiler auto-generates portable and optimized CUDA code from the MATLAB algorithm, which is then cross-compiled and deployed to the Tegra board. The workflow affords on-board real-time prototyping and verification controlled through MATLAB. Examples of common computer vision algorithms and deep learning networks are used to describe this workflow, and their performance benchmarks are presented.