embedded-ai-deployment

# 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 Codex can drive a live MATLAB and Simulink session through MCP tools. ## Workflow Pattern Selection Determine the correct workflow pattern based on model origin and deployment target. ### Decision Tree Primary discriminator for 3P models: **model size + hardware class**. ``` Q1: What is the deployment target? | +-- Cortex-M (M33, M4, M7) ---------------------> Q2 +-- Cortex-A/R processor or DSP (C2000, etc.) ----> Q2 +-- x86 processor or GPU (Jetson, CUDA) ----------> Q2 | Q2: Where does the AI model come from? | +-- Train from scratch in MATLAB ------------> Pattern 1 (references/pattern1/workflow.md) +-- Pre-trained 3P model --------------------> Q3 | Q3: Route by hardware class + model size ...