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 |

Code replacement library (CRL) — routes supported ops to compiler-vendor optimized implementations (NEON on ARM, etc.). Complementary to InstructionSetExtensions on ARM Cortex-A:

matlab
cfg.HardwareImplementation.ProdHWDeviceType = 'ARM Compatible->ARM Cortex-A';
cfg.CodeReplacementLibrary = 'GCC ARM Cortex-A';

For Cortex-M, select the CRL matching your compiler (e.g. 'ARM Cortex-M' for generic; vendor-specific CRLs are shipped with the corresponding support package). Cortex-M does not use InstructionSetExtensions.

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

MATLAB Coder vs Simulink Coder naming. Several properties above have different names when configuring the same option via set_param on a Simulink model:

| MATLAB Coder (cfg.X = ...) | Simulink Coder (set_param) | Notes | |---|---|---| | EnableOpenMP | MultiThreadedLoops | Valid on both grt.tlc and ert.tlc; both take an OpenMP-capable compiler. | | StackUsageMax | MaxStackSize | Same numeric semantics on both sides. | | DeepLearningConfig = coder.DeepLearningConfig("none") | DLTargetLibrary = "none" (codegen) + SimDLTargetLibrary = "none" (simulation) | Simulink side takes a plain string, not a config object. Set BOTH parameters — DLTargetLibrary only affects slbuild; simulation uses the separate SimDLTargetLibrary. |

This skill's examples are all MATLAB Coder (cfg.X = ...); for the Simulink side and for AI-model-specific perf knobs (reduction-loop vectorization, MEX SIMD), see matlab-deploy-ai-model/references/codegen-performance-options.md.

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 (Simulink: MaxStackSize) | | EnableOpenMP | boolean | OpenMP multi-threading (Simulink: MultiThreadedLoops) | | CodeReplacementLibrary | "GCC ARM Cortex-A", "ARM Cortex-M", … | Vendor-optimized op replacements | | 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)

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