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Aidge workflow 
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Aidge provides a comprehensive workflow to streamline the development
and deployment of AI models for edge devices. Here is an overview of a
typical workflow and the key functionalities Aidge offers.

.. image:: /source/_static/aidge_workflow.png
   :scale: 60%

.. raw:: html

   <br><br>

Aidge guides you through the process of preparing, optimizing, and
deploying your AI models:

1. **Define a model**

..

Start by loading a (pre-trained) AI model from an ONNX file, or
alternatively, define your model using Aidge's API. Either way, your
model will be accessible at any time via Aidge's graph intermediate
representation.

2. **Visualize and analyze the model**

..

Utilize Aidge's tools to `visualize <aidge_model_vis.rst>`__ and `analyze <https://eclipse.dev/aidge/source/Tutorial/static_analysis.html>`__  your model's
architecture, helping you understand its structure and identify areas
for improvement.

3. **Optimize the model (optional)**

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Train your model or apply powerful optimization techniques (such as
`quantization <https://eclipse.dev/aidge/source/Tutorial/ptq.html>`__, pruning, and simplification) to reduce model size,
improve energy efficiency, and meet the specific constraints of
embedded hardware.

4. **Save the model**

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Save your optimized model in ONNX whenever you need to.

5. **Export the model**

..

Generate the source code of your optimized model tailored for your
embedded hardware platform, making it ready for deployment.

6. **Benchmark the model**

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Evaluate the `optimized model's performance <https://eclipse.dev/aidge/source/Tutorial/benchmark.html>`__ at runtime or at execution
time to ensure it meets your performance targets on the target
hardware.

Each of these functionalities is encapsulated within `Aidge's modular structure <aidge_architecture.rst>`__.


.. toctree::
    :hidden:
    :maxdepth: 2

    aidge_architecture.rst
    aidge_runtime_vs_export.rst
    aidge_IR.rst
    aidge_core.rst
    aidge_model_vis.rst
    aidge_export.rst
    aidge_export_structure.rst