Add Machine Learning to a New or Existing Project#

This guide shows how to add a machine learning model to a Simplicity Studio project using the ML Model (ml_model) component. You will enable the AI/ML SDK extension, install the component, point it at a .tflite file in an .mlconf file, and confirm the generated headers.

The steps below use an Empty C++ project as the starting point. To add a model to an existing project, skip Step 1 and open your project's .slcp file instead.

Application code that calls the model APIs must be written in C++. If you are starting with a predominantly C application, see Interfacing with C Code.

For inference code after the model is in the project, see Machine Learning on Silicon Labs Devices from Scratch. For more than one model, see Multiple-Model Support.

Prerequisites#

• Simplicity Studio installed (screenshots from Studio 2234)

• Simplicity SDK Suite installed (e.g., v2026.6.0)

• A target board or platform selected in your workspace

• A .tflite model file on your local machine

• The AI/ML extension enabled in your workspace

Procedure#

Step 1: Create an Empty C++ Project#

1. Open Simplicity Studio and go to the PROJECTS view.

2. Open the Example Projects & Demos tab.

3. Search for empty_cpp.

4. Select Platform - Empty C++ Example and click CREATE.

The empty C++ template is a good starting point when you want to add components yourself.

Step 2: Open the Software Components Tab#

1. In Project Explorer, open your project (for example empty_cpp).

2. Open the project .slcp file.

3. Switch to the SOFTWARE COMPONENTS tab.

From here you can browse and install components, including the Silicon Labs AI/ML stack.

Step 3: Enable the AI/ML SDK Extension#

1. Search for ml_model.

2. Click on Silicon Labs AI/ML v3.x.x and then click on Enable Extension to enable Silicon Labs AI/ML extension.

Step 4: Install the ML Model Component#

1. With ml_model still in the search bar, Expand Silicon Labs AI/ML → Machine Learning and select ML Model

2. Click on Install to install the ML Model component and Create A Component Instance dialog pops up.

Step 5: Name the Component Instance#

When you click Install, Studio prompts you to create an instance:

1. Enter a name in INSTANCE NAME (for example keyword_spotting or blink). Use only valid C/POSIX filename characters — this name ends up in generated #define macros and handle names such as sl_ml_keyword_spotting_model_handle.

2. Click Done. ML Model component is now installed and and a new instance is created.

NOTE: Each .tflite model requires its own ML Model component instance. After the ML Model component is installed, add another instance with a different name (for example kws and blink) and repeat Steps 5–7 for each additional model. For initialization and runtime behavior with multiple models, see Multiple-Model Support.

Step 6: Dismiss the Initial Generation Error#

Studio should confirm the component was added. You may also see a generation error like this:

Failed to generate setup apack_aiml.mvp-compiler

ERROR:root:compile: pass a .tflite path as the model argument, or set "model" in your YAML/mlconf config.

This error is expected because the project has not yet been configured with a .tflite file path. Dismiss the dialog and proceed to the next step.

Step 7: Configure the .mlconf File#

1. In Project Explorer, open config/tflite/ and double-click your .mlconf file (for example keyword_spotting.mlconf).

2. Set model: to the absolute path of your .tflite file:

   model: C:\Users\<username>\Desktop\keyword_spotting_on_off_v2.tflitemodel: C:\Users\<username>\Desktop\keyword_spotting_on_off_v2.tflite

3. Save the file. Studio regenerates the project automatically.

NOTE: A project with more than one model has one .mlconf file per instance under config/tflite/ (for example keyword_spotting.mlconf and blink.mlconf). Set model: in each file to the .tflite path for that instance.

Step 8: Verify Generated Files#

After generation finishes:

1. Expand the autogen/ folder in Project Explorer.

2. Check for new headers named after your model, for example:

   • keyword_spotting_on_off_v2_generated.hpp

   • sl_ml_model_keyword_spotting_on_off_v2.h

3. On the SOFTWARE COMPONENTS tab, confirm your ML Model instance shows a green checkmark.

At project generation, Simplicity Studio runs the MVP Compiler on each model and produces embedded artifacts in autogen/, including:

The ML Model component includes the TensorFlow Lite Micro runtime. No automatic model initialization runs at system startup — the application loads the model when it is ready to run inference.

For microphone-based models, also add the Audio Feature Generator (fe_audio) component. See Feature Generators.

NOTE: With multiple model instances, autogen/ contains a separate generated header and handle for each instance (for example sl_ml_model_kws.h and sl_ml_model_blink.h).

Summary#

Additional Software Components and C++ Build Settings#

Note: Skip this section for Series 2 devices. It applies to SiWx917 projects only.

1. Ensure the following components are installed in your project.

   • WiseConnect SDK > Device > Si91X > MCU > Service > Power Manager > Sleep Timer for Si91x

   • Platform > Peripheral > Common Headers

   

   

2. Update your C++ build settings as follows:

   • In the C preprocessor defines, add: SUPPORT_CPLUSPLUS.

   • In GNU ARM C++ Compiler > Miscellaneous settings, add: -mfp16-format=ieee.

   

   Alternate method:

   You can also add these settings directly to your project's .slcp file:

   • Open the .slcp file in a text editor.

   • Add SUPPORT_CPLUSPLUS to the define section for C preprocessor defines. For example:

     define:
       - name: SUPPORT_CPLUSPLUS
         value: 1define:
       - name: SUPPORT_CPLUSPLUS
         value: 1

   • Add -mfp16-format=ieee to the toolchain_settings section for C++ compiler flags, for example:

     toolchain_settings:
       - option: gcc_compiler_option
         value: -mfp16-format=ieeetoolchain_settings:
       - option: gcc_compiler_option
         value: -mfp16-format=ieee

   • Save the file and regenerate your project to apply the changes.

Troubleshooting#

Next Steps#

Include the generated headers in your application and follow Machine Learning on Silicon Labs Devices from Scratch to initialize the model and run inference.

Addendum: Interfacing with C Code#

If your project is written in C rather than C++, place the code interfacing with the model APIs into a separate file that exports a C API through an interface header. For this example, a filename app_ml.cpp is assumed that implements the function ml_process_action() with the same content as in the from-scratch guide.

app_ml.h

#ifdef __cplusplus
extern "C" {
#endif

void ml_process_action(void);

#ifdef __cplusplus
}
#endif#ifdef __cplusplus
extern "C" {
#endif

void ml_process_action(void);

#ifdef __cplusplus
}
#endif

app_ml.cpp#

#include "app_ml.h"
#include "sl_ml_model_blink.h"
#include "sl_ml_tflite_micro_model.h"

extern "C" void ml_process_action(void)
{
    /* Call sl_ml_model_run() as shown in the from-scratch guide.
       Call sl_ml_model_init() from app_init(). */
}#include "app_ml.h"
#include "sl_ml_model_blink.h"
#include "sl_ml_tflite_micro_model.h"

extern "C" void ml_process_action(void)
{
    /* Call sl_ml_model_run() as shown in the from-scratch guide.
       Call sl_ml_model_init() from app_init(). */
}

app.c#

#include "app_ml.h"
// ...
void app_process_action(void)
{
    ml_process_action();
}#include "app_ml.h"
// ...
void app_process_action(void)
{
    ml_process_action();
}

Addendum: Series 2 to SiWx917 (and vice versa) App Conversion#

If you need to port an application between Series 2 and SiWx917, follow the Additional Software Components and C++ Build Settings section for SiWx917 when required. The ML Model (ml_model) component and sl_ml_model_init() / sl_ml_model_run() workflow is the same on supported boards. Regenerate the project after changing the target device.