Embedded AI for Engineered Systems

Embedded AI for Engineered Systems

Deploy AI models to embedded hardware using MATLAB® and Simulink®. This skill is written specifically for MATLAB R2026a and uses APIs, functions, and workflows introduced in that release. It covers the complete lifecycle: model creation or import, verification, compression, system-level simulation, and code generation for resource-constrained targets.

Requires MATLAB R2026a or newer. Core toolboxes: Deep Learning Toolbox, Statistics and Machine Learning Toolbox, MATLAB Coder, Embedded Coder, Simulink, and Fixed-Point Designer. Workflow-specific support packages are checked during Environment Discovery. The MATLAB and Simulink Agentic Toolkits must be available so the agent can drive a live MATLAB and Simulink session through MCP tools.

When to Use

• Deploying a trained neural network (MATLAB-native or imported) to embedded hardware
• Generating C or CUDA code from a deep learning model for ARM Cortex-M/A/R, x86, or GPU targets
• Deploying imported PyTorch, ONNX, TensorFlow, or LiteRT models to embedded targets (import step handled by /matlab-import-external-ai-model)
• Compressing AI models (quantization, pruning, projection) to fit resource-constrained hardware
• Integrating AI inference into a Simulink system model for closed-loop simulation before code generation
• Using loadPyTorchExportedProgram, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models
• Choosing between MathWorks-native code generation and direct PyTorch/LiteRT code generation

When NOT to Use

• Training a model purely for research with no deployment target — use Deep Learning Toolbox documentation directly
• Deploying to cloud/server endpoints (no embedded target) — use MATLAB Production Server or MATLAB Compiler SDK
• Working with classical ML models (decision trees, SVMs, ensembles) that aren't neural networks — use Statistics and Machine Learning Toolbox codegen workflows directly. Note: fitcnet/fitrnet neural network models ARE covered by this skill
• Generating code for non-AI Simulink models — use standard Embedded Coder workflows
• Converting between model formats without an embedded deployment goal (e.g., ONNX to MATLAB for desktop inference only)

Workflow Pattern Selection

This skill uses two deployment patterns:

• Pattern 1 — MATLAB Network Codegen: Import or build a dlnetwork, optionally compress it, then generate C/C++ via MATLAB Coder or export to Simulink. See references/pattern1/workflow.md.
• Pattern 2 — PyTorch/LiteRT Direct Codegen: Load a PyTorch (.pt2) or LiteRT