Matlab Deploy Embedded AI
Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded...
Install
npx skills add matlab/matlab-agentic-toolkit --skill matlab-deploy-embedded-ai -g -a claude-code -y- Repository
- matlab/matlab-agentic-toolkit1.2k
- Category
- DevOps
- Updated
- 8d ago
- License
- See repository
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or imported models rebuilt as dlnetwork for lean hardware, (2) direct C/C++ code generation from PyTorch and LiteRT models. Both patterns support all targets (Cortex-M/A/R, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, loadLiteRTModel, importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromTensorFlow, importNetworkFromKeras, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.
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
