AI with Model-Based Design: Virtual Sensor Modeling

Overview

The upcoming webinar focuses on a comprehensive workflow for creating AI-based virtual sensor models, covering the entire process from design to deployment on embedded processors. Participants will learn how to integrate AI models into Simulink, apply verification techniques, and optimize model performance.

Key Takeaways

Learn to design and deploy AI-based virtual sensor models in a unified environment.
Discover how to integrate AI models into Simulink for enhanced simulation and testing.
Understand the application of formal verification techniques for neural network behavior.
Gain insights into compressing AI models for improved memory efficiency and speed.
Explore the generation of library-free C code from AI models and conduct PIL tests.

Webinar Overview

This webinar is designed for engineers and developers interested in AI and embedded systems.

›Focuses on end-to-end solutions for AI-based virtual sensor modeling.
›Covers design, training, validation, verification, and deployment processes.

Participants will gain valuable insights into the workflow necessary for developing AI models that function as virtual sensors. The session will highlight practical applications and real-world scenarios.

Integration with Simulink

Integrating AI models into Simulink enhances system-level simulation capabilities.

›Enables comprehensive verification and simulation-based testing.
›Facilitates the evaluation of system performance under various conditions.

By incorporating AI models into Simulink, users can simulate the behavior of their systems more effectively. This integration allows for a more thorough testing process, ensuring that the models perform as expected in real-world applications.

Formal Verification Techniques

Formal verification is crucial for ensuring the reliability of neural networks.

›Techniques are applied to assert the behavior of AI models.
›Helps identify potential issues before deployment.

The webinar will cover various formal verification techniques that can be utilized to validate the behavior of neural networks. This step is essential to ensure that the models meet safety and performance standards before they are deployed in critical applications.

Model Compression and Performance

Optimizing AI models for efficiency is a key focus of the webinar.

›Compression techniques reduce memory footprint and enhance execution speed.
›Participants will learn how to evaluate design and model selection tradeoffs.

The session will explore methods for compressing AI models, which is vital for deploying them on embedded systems with limited resources. By understanding the tradeoffs involved in design and model selection, participants can make informed decisions to optimize their applications.

Code Generation and Testing

Generating efficient code from AI models is essential for deployment.

›Learn to generate library-free C code from AI models.
›Conduct Processor-in-the-Loop (PIL) tests to validate performance.

The webinar will demonstrate how to generate C code from AI models without relying on external libraries, which simplifies deployment. Additionally, participants will learn how to perform PIL tests to ensure that the generated code meets performance requirements.

Registration and Participation

Don't miss the opportunity to enhance your skills in AI and embedded systems.

›The webinar is free to attend.
›Registration is required to secure a spot.

Interested participants are encouraged to register for this informative webinar. This session promises to provide valuable knowledge and skills for those working with AI and virtual sensors in embedded systems.

Frequently Asked Questions

What is the focus of the webinar?

The webinar focuses on designing, training, validating, and deploying AI-based virtual sensor models.

Is there a cost to attend the webinar?

No, the webinar is free to attend, but registration is required.

What tools will be discussed in the webinar?

The webinar will discuss the integration of AI models into Simulink and the use of MATLAB for designing virtual sensors.

What are Processor-in-the-Loop (PIL) tests?

PIL tests are conducted to validate the performance of generated code by running it on the target processor while interacting with the simulation environment.

Who should attend this webinar?

This webinar is ideal for engineers and developers interested in AI applications and embedded systems.

Join us to expand your knowledge in AI and virtual sensor technology.