STT-tensorflow/tensorflow/lite/micro/kernels/cmsis-nn

Info

To use CMSIS-NN optimized kernels instead of reference kernel add TAGS=cmsis-nn to the make line. Some micro architectures have optimizations (M4 or higher), others don't. The kernels that doesn't have optimization for a certain micro architecture fallback to use TFLu reference kernels.

The optimizations are almost exclusively made for int8 (symmetric) model. For more details, please read CMSIS-NN doc

Example 1

A simple way to compile a binary with CMSIS-NN optimizations.

make -f tensorflow/lite/micro/tools/make/Makefile TAGS=cmsis-nn \
TARGET=sparkfun_edge person_detection_int8_bin

Example 2 - MBED

Using mbed you'll be able to compile for the many different targets supported by mbed. Here's an example on how to do that. Start by generating an mbed project.

make -f tensorflow/lite/micro/tools/make/Makefile TAGS=cmsis-nn \
generate_person_detection_mbed_project

Go into the generated mbed project folder, currently:

tensorflow/lite/micro/tools/make/gen/linux_x86_64/prj/person_detection_int8/mbed

and setup mbed.

mbed new .

Note: Mbed has a dependency to an old version of arm_math.h. Therefore you need to copy the newer version as follows:

cp tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/DSP/Include/\
arm_math.h mbed-os/cmsis/TARGET_CORTEX_M/arm_math.h

There's also a dependency to an old cmsis_gcc.h, which you can fix with the following:

tensorflow/lite/micro/tools/make/downloads/cmsis/CMSIS/Core/Include/\
cmsis_gcc.h mbed-os/cmsis/TARGET_CORTEX_M/cmsis_gcc.h

This issue will be resolved soon.

Now type:

mbed compile -m DISCO_F746NG -t GCC_ARM

and that gives you a binary for the DISCO_F746NG with CMSIS-NN optimized kernels.