Multiple-Model Support#

This guide describes how to run more than one machine learning model in a single Silicon Labs project. For single-model inference steps, see Machine Learning on Silicon Labs Devices from Scratch.

Setting up multiple models in Simplicity Studio#

When you need more than one model, add one ML Model (ml_model) component instance per model. Each instance gets its own .mlconf file under config/tflite/ and its own generated headers under autogen/ml/.

IMPORTANT: Follow the steps below in order. Finish Model A completely — instance, .mlconf, and generated sources — before you create Model B.

Step 1: Model A instance creation and .mlconf configuration#

For Model A instance creation and .mlconf configuration, follow Add Machine Learning to a New or Existing Project. Name the first instance (for example, blink_model), set the model: path in its .mlconf file, and confirm Model A sources appear under autogen/ml/ — for example, blink_generated.hpp and sl_ml_model_blink.h. Do not add a second instance until these files are present.

Step 2: Model B instance creation#

  1. Open your project .slcp file and go to the SOFTWARE COMPONENTS tab.

  2. Select ML Model under Silicon Labs AI/ML → Machine Learning.

  3. Click + Add New Instances, enter a name for Model B (for example, keyword_spotting_model), and confirm.

Add a second ML Model instanceAdd a second ML Model instance

Step 3: Model B .mlconf configuration#

  1. Open the new .mlconf file in config/tflite/ (for example, keyword_spotting_model.mlconf).

  2. Set model: to the absolute path of Model B's .tflite file and save.

  3. Wait for generation to finish. Confirm Model B sources appear under autogen/ml/, for example:

    • keyword_spotting_on_off_v2_generated.hpp

    • sl_ml_model_keyword_spotting_on_off_v2.h

Step 4: Verify both models#

When both models are configured, Project Explorer and the SOFTWARE COMPONENTS tab should show:

  • Two ML Model instances (for example, blink_model and keyword_spotting_model)

  • One .mlconf file per instance under config/tflite/

  • Generated source and header files for each model under autogen/ml/

Multi-model instances, mlconf files, and generated sourcesMulti-model instances, mlconf files, and generated sources

Using multiple models in an application#

Each model instance produces its own handle in autogen/sl_ml_model_<instance>.h. The handle variable name follows the instance name — for example, an instance named kws produces sl_ml_kws_model_handle, and an instance named blink produces sl_ml_blink_model_handle.

  1. Call sl_ml_model_init() during application startup in app_init(), and call sl_ml_model_run() from app_process_action().

Do not call sl_ml_model_init() on every iteration of app_process_action() — as models stay initialized until sl_ml_model_deinit() is called. Refer to the ML TFLite Micro Model API reference, for more information.

Choose how models share memory:#

Concurrent Loading for Faster Model Switching#

Initialize every required model once in app_init() and leave them loaded. Call sl_ml_model_run() on each handle from app_process_action() as needed.

NOTE:

  • Use this when a second model must run without unloading the first (for example, keyword detection always active while a second model drives an LED animation).

  • Ensure that all the models can be loaded into the available RAM.

  • If sl_ml_model_init() returns SL_STATUS_FAIL, try sequential loading instead.

void app_init(void)
{
    sl_ml_model_init(&sl_ml_kws_model_handle);
    sl_ml_model_init(&sl_ml_blink_model_handle);
}

void app_process_action(void)
{
    sl_ml_model_run(&sl_ml_kws_model_handle);
    sl_ml_model_run(&sl_ml_blink_model_handle);
}

Sequential Loading for Lower RAM Usage#

Load one model at a time. Call sl_ml_model_init() before use and sl_ml_model_deinit() before switching to another model. Use this pattern only when total RAM cannot hold every model's tensor arena at once.

void app_process_action(void)
{
    sl_ml_model_init(&sl_ml_kws_model_handle);
    sl_ml_model_run(&sl_ml_kws_model_handle);
    sl_ml_model_deinit(&sl_ml_kws_model_handle);

    sl_ml_model_init(&sl_ml_blink_model_handle);
    sl_ml_model_run(&sl_ml_blink_model_handle);
    sl_ml_model_deinit(&sl_ml_blink_model_handle);
}

Reference#