zephyr

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Stephanos Ioannidis cf211aa7af treewide: Update deprecated CONFIG_LIB_CPLUSPLUS usages This commit updates all deprecated `CONFIG_LIB_CPLUSPLUS` usages to: * check if the Zephyr minimal C++ library is enabled using `CONFIG_MINIMAL_LIBCPP` instead of relying on the `CONFIG_LIB_CPLUSPLUS`-based inference. * select `CONFIG_REQUIRES_FULL_LIBCPP` when there exists a component- level C++ standard library dependency. This allows a component to declare C++ standard library dependency without designating a specific libray implementation. * select the correct type of C++ standard library implementation to use through one of the `CONFIG_LIBCPP_IMPLEMENTATION` choices. Signed-off-by: Stephanos Ioannidis <stephanos.ioannidis@nordicsemi.no>		2023-01-13 17:42:55 -05:00
..
boards	samples: modules: tflite-micro: magic_wand: add missing CONFIG_I2C=y	2022-08-05 12:55:51 +02:00
renode
src	includes: prefer <zephyr/kernel.h> over <zephyr/zephyr.h>	2022-09-05 16:31:47 +02:00
train
CMakeLists.txt
prj.conf	treewide: Update deprecated CONFIG_LIB_CPLUSPLUS usages	2023-01-13 17:42:55 -05:00
README.rst	treewide: Use CONFIG_CPP instead of CONFIG_CPLUSPLUS	2023-01-13 17:42:55 -05:00
sample.yaml

README.rst

.. _tensorflow_magic_wand:

TensorFlow Lite Micro Magic Wand sample
#######################################

Overview
********

This sample application shows how to use TensorFlow Lite Micro
to run a 20 kilobyte neural network model that recognizes gestures
from an accelerometer.

.. Note::
    This README and sample have been modified from
    `the TensorFlow Magic Wand sample for Zephyr`_ and
    `the Antmicro tutorial on Renode emulation for TensorFlow`_.

.. _the TensorFlow Magic Wand sample for Zephyr:
    https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/examples/magic_wand

.. _the Antmicro tutorial on Renode emulation for TensorFlow:
    https://github.com/antmicro/litex-vexriscv-tensorflow-lite-demo

Building and Running
********************

The application can be built for the :ref:`litex-vexriscv` for
emulation in Renode as follows:

.. zephyr-app-commands::
   :zephyr-app: samples/tensorflow/magic_wand
   :host-os: unix
   :board: litex_vexriscv
   :goals: build
   :compact:

Once the application is built, `download and install Renode 1.12 or higher as a package`_
following the instructions in the `Renode GitHub README`_ and
start the emulator:

.. code-block:: console

    renode -e "set zephyr_elf @./build/zephyr/zephyr.elf; s @./samples/modules/tflite-micro/magic_wand/renode/litex-vexriscv-tflite.resc"

.. _download and install Renode 1.12 or higher as a package:
    https://github.com/renode/renode/releases/

.. _Renode GitHub README:
    https://github.com/renode/renode/blob/master/README.rst

Sample Output
=============

The Renode-emulated LiteX/VexRiscv board is fed data that the
application recognizes as a series of alternating ring and slope
gestures.

.. code-block:: console

    Got accelerometer, label: accel-0

    RING:
              *
           *     *
         *         *
        *           *
         *         *
           *     *
              *

    SLOPE:
            *
           *
          *
         *
        *
       *
      *
     * * * * * * * *

    RING:
              *
           *     *
         *         *
        *           *
         *         *
           *     *
              *

    SLOPE:
            *
           *
          *
         *
        *
       *
      *
     * * * * * * * *

Modifying Sample for Your Own Project
*************************************

It is recommended that you copy and modify one of the two TensorFlow
samples when creating your own TensorFlow project. To build with
TensorFlow, you must enable the below Kconfig options in your :file:`prj.conf`:

.. code-block:: kconfig

    CONFIG_CPP=y
    CONFIG_NEWLIB_LIBC=y
    CONFIG_TENSORFLOW_LITE_MICRO=y

Training
********
Follow the instructions in the :file:`train/` directory to train your
own model for use in the sample.