Matlab deploy embedded code

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

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;

• 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):

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:

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:

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):

cfg.VerificationMode = "PIL";
cfg.Hardware.PILInterface = "Serial";
cfg.Hardware.PILCOMPort = "COM4";  % adjust to your system

Cortex-A / Raspberry Pi (SSH transport):

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

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

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)

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