Deploy Embedded Code

Configure MATLAB Coder with Embedded Coder for production-quality code generation targeting embedded hardware, and verify correctness with processor-in-the-loop (PIL) testing.

When to Use

• Generating C/C++ code for a microcontroller or embedded Linux board
• Setting up PIL or SIL verification for generated code
• Configuring code generation with no dynamic memory allocation
• Configuring Embedded Coder for an AI model entry-point function prepared by matlab-deploy-embedded-ai; for the full AI compression and model-loading workflow, trigger matlab-deploy-embedded-ai first
• Selecting a hardware target board (STM32, Raspberry Pi)

When NOT to Use

• Generating MEX or desktop libraries, use standard codegen workflows
• Simulink-based deployment, use Simulink Coder / Embedded Coder workflows directly
• GPU code generation (CUDA), use GPU Coder

Workflow

1. Create an ERT-Based Configuration

cfg = coder.config("lib", "ecoder", true);

The "ecoder", true flag creates an ERT-based (Embedded Real-Time) configuration that generates production-quality code with no OS dependencies.

2. Select Target Hardware

With coder.hardware:

cfg.Hardware = coder.hardware("STM32F746G-Discovery");

Without the support package, configure hardware manually:

cfg.HardwareImplementation.ProdHWDeviceType = 'ARM Compatible->ARM Cortex-M';
cfg.HardwareImplementation.ProdBitPerFloat = 32;
cfg.HardwareImplementation.ProdBitPerDouble = 64;

See references/supported-hardware.md for the full list of supported boards and their constraints.

3. Configure Memory for Bare-Metal Targets

cfg.EnableDynamicMemoryAllocation = false;
cfg.StackUsageMax = 512;