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"Deep Learning in MATLAB: From Concept to Optimized Embedded Code," a Presentation from MathWorks

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Avinash Nehemiah, Product Marketing Manager for Computer Vision, and Girish Venkataramani, Product Development Manager, both of MathWorks, present the "Deep Learning in MATLAB: From Concept to Optimized Embedded Code" tutorial at the May 2018 Embedded Vision Summit.

In this presentation, you'll learn how to adopt MATLAB to design deep learning based vision applications and re-target deployment to embedded CPUs and GPUs. 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, those networks are trained using MATLAB's GPU and parallel computing support either on the desktop, a local compute cluster, or in the cloud. In the deployment phase, code-generation tools are employed to automatically generate optimized code that can target both embedded GPUs like Jetson TX2, DrivePX2, or Intel based CPU platforms or ARM-based embedded platforms. The generated code is highly optimized to the chosen target platform. The auto-generated code is ~2.5x faster than mxNet, ~5x faster than Caffe2, ~7x on these platforms. Nehemiah and Venkataramani use an example of LiDAR processing for autonomous driving to illustrate these concepts.