Overview
Designing deep learning and computer vision applications and deploying to embedded GPUs and CPUs like NVIDIA Jetson and DRIVE platforms is challenging because of resource constraints inherent in embedded devices. A MATLAB-based workflow facilitates the design of these applications, and automatic generated C/C++ or CUDA® code can be deployed to achieve up to 2X faster inference than other deep learning frameworks.
This webinar walks you through the workflow. Create an automotive lane and vehicle detection application using a YOLO v2 network in MATLAB running on the CPU, test it on the same desktop using a Titan V GPU, then deploy it onto a Jetson AGX Xavier. Design and deploy deep learning networks for pedestrian detection, blood smear segmentation, and defective product detection to either an Intel Xeon processor on a desktop, ARM Cortex-A processor on a Raspberry Pi, or an NVIDIA Jetson AGX Xavier. Learn how to access peripherals from the Jetson platform for use in MATLAB and with the generated code. Finally, hear about optimizations applied to the generated code that help it achieves up to 2X faster inference than other deep learning frameworks.
Highlights
Watch this webinar to learn how to:
- Generate C/C++ or CUDA code from deep learning networks as inference engines for NVIDIA GPUs, Intel Xeon CPUs, or ARM Cortex-A processors
- Generate C/C++ or CUDA code from complete applications, including one or more deep learning networks along with pre- and postprocessing code, for NVIDIA GPUs, Intel Xeon CPUs, or ARM Cortex-A processors
- Automate the process to compile and download the generated code onto NVIDIA GPUs like the Jetson AGX Xavier and NVIDIA DRIVE boards
- Access peripherals from the Jetson platform for use in MATLAB and with the generated code
- Apply optimizations to boost the performance of the generated C/C++ or CUDA code for deep learning networks
About the Presenter
Bill Chou is the Product Marketing Manager for MATLAB Coder, and has been working with MathWorks code generation technologies for the past 10 years. Bill holds an M.S. degree in Electrical Engineering from the University of Southern California and a B.A.Sc degree in Electrical Engineering from the University of British Columbia.
