embedded-ai-deploymentlisted

# Embedded AI for Engineered Systems Deploy AI models to embedded hardware using MATLAB&reg; and Simulink&reg;. 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. ## 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 | +-- Cortex-M: always Pattern 1 import | (MathWorks compression, tight sim-codegen agreement) | +-- x86 / GPU: Pattern 2 if PyTorch or LiteRT | Pattern 1 import if ONNX/TF (convert to Py/LiteRT recommended) | +-- Cortex-A/R or DSP: +-- Small model (< 500 KB) -----