STT-tensorflow/tensorflow/lite/micro/benchmarks/README.md
Nat Jeffries e225b8c8ca Create a benchmark for the person detection model.
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2020-04-21 12:46:03 -07:00

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# 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](#keyword-benchmark)
- [Person Detection Benchmark](#person-detection-benchmark)
- [Run on x86](#run-on-x86)
- [Run on Xtensa XPG Simulator](#run-on-xtensa-xpg-simulator)
- [Run on Sparkfun Edge](#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](https://sparkfun.com/products/15170).
If you're new to using this board, we recommend walking through the
[AI on a microcontroller with TensorFlow Lite and SparkFun Edge](https://codelabs.developers.google.com/codelabs/sparkfun-tensorflow)
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](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/person_detection/README.md#running-on-sparkfun-edge).