# Matlab Deploy Embedded AI

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded...

- Repository: https://github.com/matlab/matlab-agentic-toolkit (1237 stars)
- Category: DevOps
- License: see repository
- Last updated: 2026-10-27
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-deploy-embedded-ai -g -a claude-code -y`
Codex: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-deploy-embedded-ai -g -a codex -y`
Cursor: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-deploy-embedded-ai -g -a cursor -y`
Gemini CLI: `npx skills add matlab/matlab-agentic-toolkit --skill matlab-deploy-embedded-ai -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add matlab/matlab-agentic-toolkit`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/matlab/matlab-agentic-toolkit/blob/HEAD/skills-catalog/ai-and-statistics/matlab-deploy-embedded-ai/SKILL.md