d38e40dcdd
What is changed? - Updated the board qualifier for MPS3 to `mps3/corstone300/fvp` Why do we need this change? - To validate the sample with FVP by default since QEMU doesn't model the NPU. Signed-off-by: Sudan Landge <sudan.landge@arm.com>
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3.8 KiB
ReStructuredText
119 lines
3.8 KiB
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.. zephyr:code-sample:: tflite-hello-world
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:name: Hello World
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Replicate a sine wave using TensorFlow Lite for Microcontrollers.
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Overview
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********
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This sample TensorFlow application replicates a sine wave and
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demonstrates the absolute basics of using TensorFlow Lite Micro.
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The model included with the sample is trained to replicate a
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sine function and generates x values to print alongside the
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y values predicted by the model. The x values iterate from 0 to
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an approximation of 2π.
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The sample also includes a full end-to-end workflow of training
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a model and converting it for use with TensorFlow Lite Micro for
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running inference on a microcontroller.
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The sample comes in two flavors. One with TensorFlow Lite Micro
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reference kernels and one with CMSIS-NN optimized kernels.
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.. Note::
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This README and sample have been modified from
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`the TensorFlow Hello World sample for Zephyr`_.
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.. _the TensorFlow Hello World sample for Zephyr:
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https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/examples/hello_world
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Building and Running
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********************
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The sample should work on most boards since it does not rely
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on any sensors.
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Add the tflite-micro module to your West manifest and pull it:
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.. code-block:: console
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west config manifest.project-filter -- +tflite-micro
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west update
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The reference kernel application can be built and executed on QEMU as follows:
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.. zephyr-app-commands::
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:zephyr-app: samples/modules/tflite-micro/hello_world
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:host-os: unix
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:board: qemu_x86
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:goals: run
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:compact:
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Exit QEMU by pressing :kbd:`CTRL+A` :kbd:`x`.
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The CMSIS-NN kernel application can be built and executed on any Arm(R) Cortex(R)-M core based platform,
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for example based on Arm Corstone(TM)-300 software. A reference implementation of Corstone-300
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can be downloaded either as a FPGA bitfile for the
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[MPS3 FPGA prototyping board](https://developer.arm.com/tools-and-software/development-boards/fpga-prototyping-boards/mps3),
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or as a
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[Fixed Virtual Platform](https://developer.arm.com/tools-and-software/open-source-software/arm-platforms-software/arm-ecosystem-fvps)
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that can be emulated on a host machine.
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Assuming that the Corstone-300 FVP has been downloaded, installed and added to
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the :envvar:`PATH` variable, then building and testing can be done with following
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commands.
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```
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$ west build -p auto -b mps3/corstone300/fvp samples/modules/tflite-micro/hello_world/ -T sample.tensorflow.helloworld.cmsis_nn
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$ FVP_Corstone_SSE-300_Ethos-U55 build/zephyr/zephyr.elf
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```
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Sample Output
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=============
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.. code-block:: console
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...
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x_value: 1.0995567*2^1, y_value: 1.6951603*2^-1
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x_value: 1.2566366*2^1, y_value: 1.1527088*2^-1
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x_value: 1.4137159*2^1, y_value: 1.1527088*2^-2
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x_value: 1.5707957*2^1, y_value: -1.0849024*2^-6
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x_value: 1.7278753*2^1, y_value: -1.0509993*2^-2
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...
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The modified sample prints 10 generated-x-and-predicted-y pairs. To see
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the full period of the sine curve, increase the number of loops in :file:`main.c`.
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Modifying Sample for Your Own Project
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*************************************
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It is recommended that you copy and modify one of the two TensorFlow
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samples when creating your own TensorFlow project. To build with
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TensorFlow, you must enable the below Kconfig options in your :file:`prj.conf`:
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.. code-block:: cfg
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CONFIG_CPP=y
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CONFIG_REQUIRES_FULL_LIBC=y
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CONFIG_TENSORFLOW_LITE_MICRO=y
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Note that the CMSIS-NN kernel sample demonstrates how to use CMSIS-NN optimized kernels with
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TensorFlow Lite Micro, in that is sets below Kconfig option. Note also that this
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Kconfig option is only set for Arm Cortex-M cores, i.e. option CPU_CORTEX_M is set.
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.. code-block:: cfg
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CONFIG_TENSORFLOW_LITE_MICRO_CMSIS_NN_KERNELS=y
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Training
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********
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Follow the instructions in the :file:`train/` directory to train your
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own model for use in the sample.
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