Embedded AI with MATLAB and Simulink in the Era of Agentic AI Workflows
| Start Time | End Time |
|---|---|
| 27 Oct 2026, 9:00 AM EDT | 27 Oct 2026, 10:00 AM EDT |
| 27 Oct 2026, 2:00 PM EDT | 27 Oct 2026, 3:00 PM EDT |
Overview
AI is becoming a standard component in embedded systems but getting a model to run reliably on resource-constrained hardware, alongside control logic and signal processing, remains an engineering challenge.
Agentic AI tools are making it faster to build models and generate code, but they don't solve the system-level problem: does the AI behave correctly as part of the full system before it ships?
This webinar walks through a practical Embedded AI workflow using MATLAB and Simulink: from importing AI models developed in any framework, to validating behavior through system-level simulation, to generating production-ready embedded code for MCUs. You'll also see how the MATLAB MCP Server brings agentic AI tools into this workflow, so engineers can move faster without sacrificing the verification and traceability that production deployment demands.
Highlights
- Train models in MATLAB or import from PyTorch, TensorFlow, or other frameworks (ONNX), and integrate them alongside control logic, signal processing, and application software
- System-level simulation: validate AI behavior in full system context using Simulink, building confidence that the model performs as intended before committing to hardware
- Generate production-ready code: Deploy AI as part of a verified engineered system on MCUs without manual code translation
- Connect AI agents to MATLAB: Use the MATLAB MCP Server to bring agentic speed into a deterministic, traceable engineering workflow, enabling AI agents to execute code, run tests, and access toolbox capabilities within the verified Embedded AI workflow
We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.
About the Presenter
Roman Silen is a Product Marketing Engineer at MathWorks focused on AI applications for engineering. His work spans agentic AI, Embedded AI, and image processing and computer vision, where he collaborates with customer-facing and development teams to communicate technical workflows and product capabilities using MATLAB and Simulink. He holds an M.S. in Computer Science from the University of Texas at Austin focusing on machine learning and optimization and a B.S. in Mathematics from Rensselaer Polytechnic Institute.
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