Optimize the Model
==================

With both model and calibration dataset available, you can use the **eIQ AI Toolkit** Optimize & Convert feature
to prepare your model for deployment on NXP hardware.

.. image:: /_static/eIQ_AIHub_optimize.gif
   :alt: Optimize Model
   :width: 100%

What the AI Toolkit optimize&convert Does
-----------------------------------------

The Optimize and convert tool runs conversion and optimization pipelines on your model:

* Applies quantization (e.g., int8)
* Performs graph-level modifications
* Convert operators to optimized kernels for NXP NPU accelerators (Neutron, Vela)
* Reduces latency and memory footprint
* Produces deployment-ready model artifacts

Using the Calibration Dataset
-----------------------------

The calibration dataset allows the Optimizer to learn representative activation ranges for
post-training quantization. Using a high-quality calibration dataset:

* Improves accuracy retention
* Reduces quantization error
* Generates more reliable profiling and benchmarking results

Optimize and Convert a Model
----------------------------

The Optimize & Convert page provides a visual workflow canvas where you build a pipeline
by connecting your model to one or more conversion steps.

**Steps:**

1. Switch to the **AI Toolkit** tab in the top navigation bar.
2. In the left sidebar, under **Model manipulation**, click **Optimize & Convert**.
3. On the workflow canvas, your uploaded model appears as a node. If not, drag your model
   from the available resources onto the canvas.
4. Click the ``+`` button on the canvas to open the **Select step** dialog.
5. Choose a conversion step. The available steps and their compatibility depend on your
   model format:

   - **NeutronConversion** — converts a TFLite model to run on the Neutron accelerator.
   - **VelaConversion** — optimizes a TFLite model for NXP Ethos-U NPU.
   - **OnnxConversion** — converts a PyTorch model to ONNX format.
   - **TFLiteConversion** — converts an ONNX model to TFLite format.
   - **ONNX2Quant** — quantizes an ONNX model.

   Enable **Show incompatible** to see all steps, including those not compatible with your
   current model.

6. Configure the conversion parameters:

   - **Target** — select the target device (e.g., ``imxrt700``).
   - **Flavor (version)** — select the SDK or toolchain version (e.g., ``MCUXpresso SDK 26.03``).
   - Optional checkboxes such as **Export output model as header file**, **Use sequencer**, or
     **Fetch constants to SRAM**, depending on the conversion type.

7. Click **Add** to add the step to the pipeline. The step node appears on the canvas,
   connected to your model node.
8. Click the **Run** button (blue play icon) in the bottom-right corner of the canvas to
   start the pipeline.
9. A confirmation message appears: *"Your pipeline is running..."*. You can:

   - Click **Optimizations History** to monitor the pipeline status.
   - Click **Run another** to start a new pipeline.

10. When the pipeline completes, a **Copying to My models** dialog appears. Enter a name
    for the optimized model (e.g., ``my_model_neutronconversion``) and click **Copy**.
11. The optimized model now appears in your model list under **AI Hub**. Click it to open the
    **Model Detail** page, where you can view model information, visualize the architecture,
    download the model file, or delete it.

Available Conversion Steps
~~~~~~~~~~~~~~~~~~~~~~~~~~

.. list-table::
   :header-rows: 1
   :widths: 25 20 25 30

   * - Step
     - Input Format
     - Output Format
     - Description
   * - NeutronConversion
     - TFLite
     - TFLite
     - Convert for Neutron accelerator
   * - VelaConversion
     - TFLite
     - TFLite
     - Optimize for Ethos-U NPU
   * - OnnxConversion
     - PyTorch
     - ONNX
     - Convert PyTorch to ONNX
   * - TFLiteConversion
     - ONNX
     - TFLite
     - Convert ONNX to TFLite
   * - ONNX2Quant
     - ONNX
     - ONNX
     - Quantize ONNX model

After optimization, the AI Hub automatically registers all generated artifacts so they can be
used in downstream steps such as profiling and benchmarking.

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

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

* :doc:`Run simulated profiling<./sim_profiling>`