Integrating AI into Model-Based Design
Deep learning and traditional machine learning techniques can solve complex problems that traditional methods can’t adequately model, such as detecting objects in an image or accurately predicting battery state-of-charge based on current and voltage measurements. While these capabilities by themselves are remarkable, the AI model typically represents only a small piece of a larger system. For example, embedded software for self-driving cars may have different adaptive cruise control, lane keep control, sensor fusion, lidar logic, and many other components in addition to deep learning-based computer vision. How do you integrate, implement, and test all these different components together while minimizing expensive testing with actual hardware and the vehicle?
In this session, you will learn how to use AI with Model-Based Design to make the complexity of such systems more manageable, use simulation for adequate testing, and deploy to targeted hardware (ECU, CPU, and GPU) using code generation. You will see this approach through a few industry examples.
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