Uploaded November 2025 | Updated September 2026, 3 weeks ago
NVIDIA GPUs are the hardware of choice for many applications including AI, embedded vision, and radar and signal processing algorithms. MATLAB is the ideal environment for exploring, developing, and prototyping algorithms. In this seminar, we will learn how to generate CUDA code directly from MATLAB and Simulink to run on NVIDIA GPUs using GPU Coder.
We'll walk you through the workflow that starts with designing and simulating various applications, including defective product detection and lane/vehicle detection, in MATLAB and Simulink running on the CPU, testing it on the same machine using an RTX GPU, then deploying it onto a Jetson AGX Orin. Learn how to access peripherals from the Jetson platform for use in MATLAB/Simulink and with the generated code.
Related resources:
- White Paper: Generating CUDA Code from MATLAB: Accelerating Embedded Vision and Deep Learning Algorithms on GPUs: bit.ly/2VaHN3P
- Deploy MATLAB and Simulink to NVIDIA GPUs: bit.ly/46RpXdH
Chapters:
00:00 Introduction
00:34 Example: Automated Optical Inspection
06:13 What is GPU Coder?
13:23 Example: Using GPU Coder for Automated Optical Inspection
21:00 Example: Using GPU Coder Performance Analyzer
28:19 Deep Learning Workflow
32:34 Key Take-Aways
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Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2025 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
NVIDIA GPUs are the hardware of choice for many applications including AI, embedded vision, and radar and signal processing algorithms. MATLAB is the ideal environment for exploring, developing, and prototyping algorithms. In this seminar, we will learn how to generate CUDA code directly from MATLAB and Simulink to run on NVIDIA GPUs using GPU Coder.
We'll walk you through the workflow that starts with designing and simulating various applications, including defective product detection and lane/vehicle detection, in MATLAB and Simulink running on the CPU, testing it on the same machine using an RTX GPU, then deploying it onto a Jetson AGX Orin. Learn how to access peripherals from the Jetson platform for use in MATLAB/Simulink and with the generated code.
Related resources:
- White Paper: Generating CUDA Code from MATLAB: Accelerating Embedded Vision and Deep Learning Algorithms on GPUs: bit.ly/2VaHN3P
- Deploy MATLAB and Simulink to NVIDIA GPUs: bit.ly/46RpXdH
Chapters:
00:00 Introduction
00:34 Example: Automated Optical Inspection
06:13 What is GPU Coder?
13:23 Example: Using GPU Coder for Automated Optical Inspection
21:00 Example: Using GPU Coder Performance Analyzer
28:19 Deep Learning Workflow
32:34 Key Take-Aways
--------------------------------------------------------------------------------------------------------
Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2025 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
