Deploying Deep Learning Models on Embedded Processors for Autonomous Systems with MATLAB

Learn how to bring the power of deep neural networks to memory- and power-constrained devices like those used in robotics and automated driving. The workflow starts with an 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, the 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, Jetson Drive AGX Xavier, Intel-based CPU platforms or ARM-based embedded platforms. The generated code leverages target-specific libraries that are highly optimized for the target architecture and memory model.