Generate Standalone C Code for Neural Networks with MATLAB Embedded Coder

Moving From Trained Models to Embedded Production Code

Training neural networks in MATLAB is easy, but integrating them into industrial microcontrollers requires clean, ANSI-compliant C code without external dependencies. MATLAB Embedded Coder translates layers, activations, and weight arrays into static C functions suitable for automotive ECUs, medical instruments, and industrial controllers.

Why Plain C Codegen Matters for Embedded Targets

Most neural network runtimes depend on heavy C++ runtimes, dynamic heap allocations, or OS-level threading. Embedded Coder avoids those constraints:

• Allocates buffers statically at compile time (zero malloc calls).
• Generates readable, MISRA-C compliant source code.
• Exports trained network weights directly into C header arrays stored in Flash memory.
• Does not require an underlying operating system or Python interpreter.

Configuring Codegen for Neural Networks

To produce standalone C code, define an entry point and configure the code generator with static typing.

% Step 1: Create an entry-point wrapper function
% Save this as nn_infer.m
function out = nn_infer(in)
%#codegen
persistent netObj;
if isempty(netObj)
    netObj = coder.loadDeepLearningNetwork('trained_classifier.mat');
end
out = predict(netObj, in);
end

Setting the Coder Configuration Object

Define a static library target and configure memory bounds:

% Step 2: Build the codegen script
cfg = coder.config('lib');
cfg.TargetLang = 'C';
cfg.FilePartitionMethod = 'SingleFile'; % Keep code clean for single MCU file
cfg.GenerateReport = true;

% Specify Deep Learning target without vendor proprietary libraries
dlcfg = coder.DeepLearningConfig('none');
cfg.DeepLearningConfig = dlcfg;

% Specify fixed input size (e.g., 3-channel 32x32 image)
inputExample = coder.typeof(single(0), [32 32 3]);

% Generate source code
codegen -config cfg nn_infer -args {inputExample}

Inspecting the Generated Output

Open the generated HTML code report. The generator creates:

• nn_infer.c / nn_infer.h: Primary inference logic containing matrix multiplication and activation layers.
• nn_infer_data.c: Constant weight and bias arrays placed into read-only program memory (Flash).
• nn_infer_initialize.c: Startup routine to prepare internal state variables.

Because there is no dynamic memory allocation, the entire RAM consumption of your model can be verified from the compiler's .bss and .data section sizes before flashing.