AI with Model-Based Design: Virtual Sensor Modeling

MathWorks hosted a webinar demonstrating an end-to-end workflow for designing, training, and deploying AI-based virtual sensor models to embedded processors within a single environment. The session highlighted techniques for integrating AI models into Simulink for system-level simulation, applying formal verification to neural networks, and compressing models for reduced memory footprint and faster execution. Attendees learned how to generate library-free C code from AI models, perform processor-in-the-loop tests, and profile code performance to evaluate design tradeoffs.

This webinar presents a workflow offering end-to-end solutions for designing, training, validating and verifying, compressing, and deploying AI-based virtual sensor models to embedded processors within a single environment. Highlights - Integrate AI models into Simulink for system-level simulation, verification, and simulation-based testing - Apply formal verification techniques to assert neural network behavior - Compress the AI model for memory footprint reduction and execution speedup - Generate library-free C code from AI models and performing PIL tests - Profile code performance and evaluate design and model selection tradeoffs - Design and train AI-based virtual sensors using MATLAB