.. _pcie_mode_app_dev:

PCIe Mode App
#############

In `PCIe mode <../overview/target_architectures.html#sima-devkit-as-pcie-card>`_, once a pipeline is developed, you must create a host-side application and package the MPK for deployment on the host system.  
SiMa provides :ref:`C++ <pcie_host_mode_cpp_api_reference>` and :ref:`Python APIs <pcie_host_mode_python_api_reference>` on Ubuntu x86 platforms to streamline host application development.  

Starting with SDK 1.7, you can also use the :ref:`Co-Processing <pcie_host_mode_coprocessing_api_reference>` API to deploy and manage pipeline lifecycles, as well as perform device firmware updates programmatically.

.. note::

    These APIs become accessible after `installing the PCIe driver <../overview/setup_pcie_mode.html#driver-installation>`_. 

    In the PCIe mode, you can currently use the Machine Learning Accelerator (MLA) to run inference tasks (``quant`` -> ``NN Model`` -> ``dequant``). 
    Additionally, with our support, you can manually generate the MPK in the SDK to enable any valid GStreamer PCIe pipeline. 
    
    In a future release, this mode will expand to include access to all hardware blocks, such as video codecs, enabling pre- and post-processing operations directly on the MLSoC. 
    This enhancement is part of our ongoing roadmap.

Refer to this :ref:`example <build_host_app_with_cpp>` to create a host-side application that leverages the SiMa MLSoC for running a ResNet-50 workload.

.. image:: media/pcie-mode-programming-model.jpg
    :alt: PCIe Mode Description
    :scale: 35%


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

   build_host_app_with_cpp.rst