2.3 KiB
TFLite for Microcontrollers Benchmarks
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.
Table of contents
- Keyword Benchmark
- Person Detection Benchmark
- Run on x86
- Run on Xtensa XPG Simulator
- Run on Sparkfun Edge
Keyword benchmark
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.
Person detection benchmark
The keyword benchmark provides a way to evaluate the performance of the 250KB visual wakewords model.
Run on x86
To run the keyword benchmark on x86, run
make -f tensorflow/lite/micro/tools/make/Makefile TAGS=posix test_keyword_benchmark
To run the person detection benchmark on x86, run
make -f tensorflow/lite/micro/tools/make/Makefile TAGS=posix test_person_detection_benchmark
Run on Xtensa XPG Simulator
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-xpg XTENSA_CORE=<xtensa core> TAGS=xtensa_hifimini test_keyword_benchmark -j18
Run on Sparkfun Edge
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.