Run MCU Profiling
=================

Before deploying to MCU hardware, you can use the MCU Profiling Analysis feature to
analyze profiling results from your physical MCU device directly in the eIQ AI Hub.

.. image:: /_static/eIQ_AIHub_mcu_profiling.gif
   :alt: MCU Profiling Analysis
   :width: 100%

What is MCU Profiling Analysis?
-------------------------------

MCU Profiling Analysis allows you to upload and analyze profiling results generated on
your physical MCU device. This tool does not perform profiling runs on its own — you use
`MCUXpresso <https://www.nxp.com/mcuxpresso>`_ to run the profiling externally, and then
upload the resulting JSON file to the AI Hub for parsing and analysis.

The tool provides:

* Per-node execution time analysis
* Operator-level profiling statistics
* Total inference time estimation
* Tensor arena size information
* Visual breakdown of model performance on MCU targets

Upload a Profiling JSON File
-----------------------------

The MCU Profiling Analysis page lets you upload a JSON profiling output file generated
by MCUXpresso and analyze the results in the AI Hub.

**Steps:**

1. Switch to the **AI Toolkit** tab in the top navigation bar.
2. In the left sidebar, under **Model evaluation**, click **MCU profiling analysis**.
3. On the MCU profiling analysis page, review the information box explaining the workflow.
4. Upload your profiling JSON file:

   - Drag and drop the ``.json`` file into the upload area, or click **browse** to select
     the file from your local machine.
   - After uploading, the file name appears in the upload area. Click **Remove** to
     delete it if needed.

5. Optionally, enter a **Custom run name** to label this profiling session.
6. Click the **Analyse profiling** button to start the analysis.

Review Profiling Results
-------------------------

After the analysis completes, navigate to **Profiling history** in the left sidebar
to view the results. Click on the entry to open the detailed profiling report.

**Session metadata includes:**

* **Type** — ``MCU``
* **Target** — target device information
* **Engine** — NPU engine used
* **Tensor arena size** — memory arena allocated for tensor operations
* **Model size** — size of the model file
* **Total inference time** — total inference time in milliseconds

**Per-node profiling statistics table:**

* **Node id** — unique identifier for each operator node
* **Name** — name of the operator node
* **Order** — execution order of the node
* **Op name** — type of operation (e.g., ``CONV_2D``, ``RESHAPE``, ``FULLY_CONNECTED``)
* **Input shape** — input tensor dimensions
* **Execution time** — execution time in milliseconds for that node

Use these results to identify performance bottlenecks and validate that the model meets
your latency requirements for MCU deployment.

.. note::
   Please refer to AI Toolkit document for detailed information.

Next Steps
----------

* :doc:`Profile on real hardware<./ondevice_profiling>`
* :doc:`Benchmark the model<./benchmark>`