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Embedded AI Tools

The Embedded AI Tools feature allows you to create model configurations for your project. You can add model files, assign them to cores, define engine-specific settings, and generate deployable C/C++ code. You can also run compatibility and profiling reports to check memory usage, operator support, and performance characteristics.

Use this feature to manage model deployment in multi-core environments, run compatibility and profiling checks, and generate engine-specific code for supported SoCs.

Before you start

For full compatibility when using this feature, create a new workspace and select an AI-based template (for example, Zephyr Single Core TensorFlow AI Model for MAX32690). For details on supported processors, see supported processors and model formats.

Add a model configuration

  1. Click Add Model. Embedded AI Tools tab Embedded AI Tools tab
  2. Select a Target core. For example, ARM Cortex-M4.
  3. Enter a Model Name. This name is used to identify the model configuration.

  4. Click Browse to upload and select a model file, or enter a relative file path or URL.

    Note

    For reference on how to train PyTorch models compatible with MAX78002, see the open source ai8x-synthesis repository.

  5. For TFLM models, you can optionally:

    1. Enter a Memory section for data. Specify the name of the linker section where model data should be placed (for example, .data, .flash).

      Note

      Match the section specified to the one provided in your linker file, or leave it blank to use the default data section.

    2. Specify a Symbol for data. This defines the C variable name used in the generated code for referencing the model.

  6. Click Add to add the model to the list.

Using AI models with non-AI templates

If you create a workspace for a supported processor and choose a non-AI template (such as Zephyr Single Core Blinky), note that the application source file is main.c.

When you add a TensorFlow Lite Micro (TFLM) model using Add Model, the generated model files are C++ (.cpp and .hpp). This can cause compiler warnings or errors if your project uses a C-only source (main.c).

To fix this:

  • Replace main.c with main.cpp in your project to convert it to C++, or
  • Add a C++ wrapper source file that exposes a C interface to the model.

Field descriptions

Field Description
Target The processing core that will run the model. For example, ARM Cortex-M4.
Model Name Name used to identify the model configuration.
Model File The path or URL of the model file. The path can be absolute or relative to the workspace.
Memory section for data (TFLM only). The memory section used to map the model data.
Symbol for data (TFLM only). C symbol name used for the model data array. Appears in the generated code.
Compatibility Indicates whether the model can fit and run on the selected target.
Include toggle Enables or disables file generation for the model. Toggling a model off keeps the configuration but does not generate code.

Note

Disabling the Include toggle does not remove any previously generated files. These will remain in your project unless overwritten by a subsequent generation, or deleted manually.

Reports: Compatibility and profiling

Indicates whether the model is fully supported on the selected target and provides a link to a profiling report.

  • Click Open Compatibility Report to view memory constraints, operator issues, and data type compatibility. For details on interpreting the report, see Compatibility Analyzer Report.
  • Click Open Profiling Report to view a summary of resource usage and optimization opportunities. For details on interpreting the report, see Resource Profiling Report.

Important

These reports are for guidance only and do not prevent code generation. Please note that this is an early version of the reporting feature, and it may evolve in future releases. Currently, only TFLM models are supported. CNN accelerator models on MAX78002, which use PyTorch, are not supported by the analysis tools. These options are disabled in the UI when such a model is selected.

Modify a model configuration

  1. Locate the model in the list.
  2. Click configure Configure Configure to open the Configure Model sidebar.
  3. Apply your changes, then click Update.

Remove a model configuration

When you remove a model configuration, it is deleted from the list and excluded from future code generation. Existing files will not be removed.

To remove a model configuration:

  1. Locate the model in the list.
  2. Click delete Delete Delete.
  3. Click Confirm.

Note

Deleting a model configuration does not remove any previously generated files. These will remain in your project unless overwritten by a subsequent generation.

Next steps

Before you generate code, enable the Include toggle for each AI model configuration you want to include in the generation.

If the number of AI models per core is restricted (for example, the MAX78002 CNN accelerator supports only one model), the UI automatically deselects excess configurations.

When you are ready, proceed to Generate Code.