---
name: matlab-deploy-embedded-code
description: >
Deploy MATLAB-generated code to embedded hardware using Embedded Coder. Use when
configuring code generation for microcontrollers (STM32, Raspberry Pi, ARM Cortex),
setting up PIL/SIL verification, disabling dynamic memory allocation, or configuring
hardware-specific code generation settings. Covers ERT-based configurations,
processor-in-the-loop testing, memory constraints, and the MEX→SIL→PIL verification
progression.
license: MathWorks BSD-3-Clause
metadata:
author: MathWorks
version: "1.0"
---
# 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
- Deploying deep learning models to resource-constrained hardware
- 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
```matlab
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`:
```matlab
cfg.Hardware = coder.hardware("STM32F746G-Discovery");
```
Without the support package, configure hardware manually:
```matlab
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
```matlab
cfg.EnableDynamicMemoryAllocation = false;
cfg.StackUsageMax = 512;
```
- `EnableDynamicMemoryAllocation = false` — disables `malloc`/`free` for targets
where heap is unavailable or non-deterministic. All arrays must be bounded at
compile time.
- `StackUsageMax` — set based on target SRAM. The code generation report shows
actual usage after compilation.
For entry-points that use deep learning inference (`invoke`, `predict`):
```matlab
cfg.DeepLearningConfig = coder.DeepLearningConfig('none');
cfg.LargeConstantGeneration = "KeepInSourceFiles";
```
- `DeepLearningConfig('none')` — generates C with no external DL library dependencies
(MKL-DNN, cuDNN, TensorRT). Required for bare-metal targets. Without this, codegen
may attempt to link an unavailable library and fail.
- `LargeConstantGeneration = "KeepInSourceFiles"` — keeps weight constants in source
files rather than separate data files. Needed for bare-metal targets where external
data file linking is unsupported.
### 4. Configure Performance (SIMD and OpenMP)
**SIMD vectorization** — generates vectorized code for the target ISA:
```matlab
cfg.InstructionSetExtensions = 'Neon v7'; % ARM Cortex-A (128-bit, 4x float32)
```
| Target | Value | Notes |
|--------|-------|-------|
| ARM Cortex-A (Raspberry Pi) | `'Neon v7'` | 128-bit SIMD |
| Intel x86-64 | `'SSE'`, `'SSE4.1'`, `'AVX'`, `'AVX2'`, `'AVX512F'` | Match target CPU |
| ARM Cortex-M | Do not set — use `CodeReplacementLibrary` instead | Different mechanism |
**OpenMP multi-threading** — enables parallel loops in generated code:
```matlab
cfg.EnableOpenMP = true; % multi-core targets (Cortex-A, x86)
cfg.EnableOpenMP = false; % single-core targets (Cortex-M) — no OS/threading support, won't compile
```
### 5. Set Up PIL Verification
PIL compiles the generated code, deploys it to the physical board, sends test
vectors, and compares outputs against MATLAB. This catches precision differences,
stack overflows, and memory issues that SIL cannot detect.
**Cortex-M (serial transport):**
```matlab
cfg.VerificationMode = "PIL";
cfg.Hardware.PILInterface = "Serial";
cfg.Hardware.PILCOMPort = "COM4"; % adjust to your system
```
**Cortex-A / Raspberry Pi (SSH transport):**
```matlab
cfg.VerificationMode = "PIL";
cfg.Hardware = coder.hardware("Raspberry Pi");
cfg.Hardware.DeviceAddress = "192.168.1.10";
cfg.Hardware.Username = "<your-pi-username>";
cfg.Hardware.Password = "<your-pi-password>";
cfg.Hardware.BuildDir = "/home/pi/mymodel"; % optional: defaults to /home/pi/MATLAB_ws/<release>
```
Pi PIL runs over SSH (not serial). The support package uses `DeviceAddress`,
`Username`, and `Password` to establish the SSH connection. `BuildDir` specifies
where the compiled binary is deployed on the target; if omitted, defaults to
`/home/pi/MATLAB_ws/<release>/`. Do not set `PILInterface` or `PILCOMPort` — those
are for serial-connected bare-metal boards only.
### 6. Generate Code
```matlab
cfg.TargetLang = "C";
codegen -config cfg -args {inputArgs} myEntryPoint
```
### 7. Verify with the MEX → SIL → PIL Progression
For confidence in deployment, follow this sequence:
1. **MEX** — verify on host, fast iteration
2. **SIL** (Software-in-the-Loop) — run generated code on host, compare to MATLAB
3. **PIL** (Processor-in-the-Loop) — run on actual hardware, compare to MATLAB
```matlab
cfgSil = coder.config("lib", "ecoder", true);
cfgSil.VerificationMode = "SIL";
codegen -config cfgSil -args {inputArgs} myEntryPoint
```
## Key Properties
| Property | Values | Purpose |
|----------|--------|---------|
| `VerificationMode` | `"PIL"`, `"SIL"`, `"None"` | Enable in-the-loop verification |
| `Hardware` | `coder.hardware(boardName)` | Select target board |
| `Hardware.PILInterface` | `"Serial"` | PIL communication type |
| `Hardware.PILCOMPort` | `"COM4"`, `"/dev/ttyACM0"` | Serial port |
| `EnableDynamicMemoryAllocation` | `true` (default), `false` | Master switch for heap |
| `DynamicMemoryAllocationThreshold` | numeric (bytes), default 65536 | Arrays above this use heap |
| `LargeConstantGeneration` | `"KeepInSourceFiles"`, `"WriteOnlyDNNConstantsToDataFiles"` | Where to put large constants |
| `StackUsageMax` | numeric (bytes) | Stack limit for generated code |
| `TargetLang` | `"C"`, `"C++"` | Output language |
## Common Mistakes
| Mistake | Why It's Wrong | Correct Approach |
|---------|---------------|-----------------|
| `DynamicMemoryAllocation = "Off"` | Wrong property name and type | `EnableDynamicMemoryAllocation = false` (boolean) |
| Skipping SIL before PIL | PIL failures on hardware are harder to debug | Always validate with SIL first |
| Not setting `StackUsageMax` | Default may exceed target SRAM | Set explicitly based on hardware constraints |
| Using `cfg = coder.config("lib")` without `"ecoder", true` | Creates a generic config, not ERT-based | Always pass `"ecoder", true` for embedded targets |
## Conventions
- Always: use `coder.config("lib", "ecoder", true)` for embedded targets
- Always: disable dynamic memory for bare-metal Cortex-M targets
- Always: follow MEX → SIL → PIL verification order
- Never: use `DynamicMemoryAllocation` (wrong property name — it's `EnableDynamicMemoryAllocation`)
- Prefer: `TargetLang = "C"` for Cortex-M targets (smaller code footprint)
## References
- `references/supported-hardware.md` — board specs, support packages, and PIL interface details
## See Also
- `matlab-deploy-ai-model` — full AI model codegen pipeline (load, verify, generate MEX/lib)
----
Copyright 2026 The MathWorks, Inc.
----