Generate tflite micro code which bypasses the interpreter (directly calls into kernels)

Basically this code uses a fully set up tflite micro instance to dump the internal allocations and function calls assigned to the model, then dumps the tensor and node settings into a compilable file, eliminating the need for running the interpreter at each program start and for resolving the correct kernel at run time.

An in depth explanation of the motivation and benefits is included in the matching RFC.

Below the two methods of incorporating the TensorFlow sources into your build are explain. The basic flow of building with CMake is

mkdir build
cd build
cmake [options] ..
make

The examples cmake here is by default not included due to issues with TensorFlow source code compatibility issues when using specific code versions. To enable building the examples pass -DTF_EXAMPLES=ON to CMake.

To pull the TensorFlow sources using CMake with the variable GET_TF_SRC set to ON.

e.g.

cmake -DGET_TF_SRC=ON ..

This will retrieve the TensorFlow master branch's code. It should also be noted that GET_TF_SRC is prioritized over TF_DIR (see below). If you want to specify a TensorFlow tag to checkout then this can be passed to CMake using the option TF_TAG.

e.g.

cmake -DGET_TF_SRC=ON TF_TAG=v2.2.0 ..

Similarly a Git commit hash can be provided using TF_COMMIT. Note that TF_TAG takes precedence if both are provided.

e.g.

cmake -DGET_TF_SRC=ON TF_COMMIT=0fecf6f89fd7bacc1ec4213b946a254e885b82ac ..

To checkout a different TensorFlow code base without clearing the CMake cache the argument TF_RECACHE should be set, this will force the TensorFlow source to be checked-out again.

e.g.

cmake -DGET_TF_SRC=ON -DTF_RECACHE=ON TF_COMMIT=0fecf6f89fd7bacc1ec4213b946a254e885b82ac ..

By default CMake looks for the TensorFlow source in the directory ../tensorflow. If you want to specify you TensorFlow source directory this can be done by providing the argument TF_DIR.

e.g.

cmake -DTF_DIR=../../my_tf_source ..

To invoke clang-format CMake provides the format target.

e.g.

cmake ..
make format

• check out tensorflow master next to this project (in ../tensorflow)

• start with building the tflite micro library as described in https://www.tensorflow.org/lite/microcontrollers/library:

  • cd ../tensorflow

  • make -f tensorflow/lite/micro/tools/make/Makefile hello_world_bin [optionally add BUILD_TYPE=debug]

• now run make in this project to get the compiler

• the compiler is invoked as ./compiler input.tflite output.cpp [prefix]

  e.g.

  ./compiler hello_world.tflite hello_compiled.cpp hello_

• for a quick view into the generated code see compiled_hello.cpp

  You can compare calling into interpreter and compiled code between hello_world.cc and hello_world2.cc

• The example directory contains a collection of traditional tflite micro and compiled versions:

  • hello_world: Standard tflite micro example
  • cifar10: Computer vision CNN example

• no support for big endian machines, yet