These benchmarks are for measuring the performance of key models and workloads. They are meant to be used as part of the model optimization process for a given platform.
The keyword benchmark contains a model for keyword detection with scrambled weights and biases. This model is meant to test performance on a platform only. Since the weights are scrambled, the output is meaningless. In order to validate the accuracy of optimized kernels, please run the kernel tests.
The keyword benchmark provides a way to evaluate the performance of the 250KB visual wakewords model.
To run the keyword benchmark on x86, run
make -f tensorflow/lite/micro/tools/make/Makefile run_keyword_benchmark
To run the person detection benchmark on x86, run
make -f tensorflow/lite/micro/tools/make/Makefile run_person_detection_benchmark
To run the keyword benchmark on the Xtensa XPG simulator, you will need a valid Xtensa toolchain and license. With these set up, run:
make -f tensorflow/lite/micro/tools/make/Makefile TARGET=xtensa OPTIMIZED_KERNEL_DIR=xtensa TARGET_ARCH=<target architecture> XTENSA_CORE=<xtensa core> run_keyword_benchmark -j18
The following instructions will help you build and deploy this benchmark on the SparkFun Edge development board.
If you're new to using this board, we recommend walking through the AI on a microcontroller with TensorFlow Lite and SparkFun Edge codelab to get an understanding of the workflow.
Build binary using
make -f tensorflow/lite/micro/tools/make/Makefile TARGET=sparkfun_edge person_detection_benchmark_bin
Refer to flashing instructions in the Person Detection Example.
For more info about the Corstone-300 software see: tensorflow/lite/micro/cortex_m_corstone_300/README.md.
Disclaimer: Executing the benchmark test on the Corstone-300 software will provide a general metric of instructions executed. The estimates are not cycle accurate, however it aligns to instruction per cycle, and is a consistent environment. This means it can detect if code changes changed performance.
The person detection benchmark can also run with Ethos-U enabled, as the downloaded model will be optimized for Ethos-U. For more info see: tensorflow/lite/micro/kernels/ethos_u/README.md.
To run the keyword benchmark on FVP:
make -j -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 run_keyword_benchmark
To run the person detection benchmark on FVP:
make -j -f tensorflow/lite/micro/tools/make/Makefile TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 run_person_detection_benchmark
To run the person detection benchmark on FVP with Ethos-U:
make -j -f tensorflow/lite/micro/tools/make/Makefile CO_PROCESSOR=ethos_u TARGET=cortex_m_corstone_300 TARGET_ARCH=cortex-m55 run_person_detection_benchmark