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# Performance Benchmark numbers
This document contains the performance benchmark numbers for running a few well
known models on some Android and iOS devices.
The benchmark numbers were generated by running the [TFLite benchmark
binary](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark)
on Android and running the [iOS benchmark
app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios)
on iOS.
# Android benchmarks
When running Android benchmarks, the CPU affinity is set to use big cores on the
device to reduce variance (see
[details](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark#reducing-variance-between-runs-on-android)).
Models are assumed to have been downloaded from the link, unzipped and pushed to
`/data/local/tmp/tflite_models` folder. The benchmark binary is built according
to instructions listed
[here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark#on-android).
and is assumed to have been pushed to `/data/local/tmp`.
The following command was used to run the benchmark:
```
adb shell taskset ${CPU_MASK} /data/local/tmp/benchmark_model \
--num_threads=1 \
--graph=/data/local/tmp/tflite_models/${GRAPH} \
--warmup_runs=1 \
--num_runs=50 \
--use_nnapi=false
```
where `${GRAPH}` is the name of model and `${CPU_MASK}` is the CPU affinity
chosen according to the following table:
Device | CPU_MASK |
-------| ----------
Pixel 2 | f0 |
Pixel xl | 0c |
<table>
<thead>
<tr>
<th>Model Name</th>
<th>Device </th>
<th>Mean inference time (std dev)</th>
</tr>
</thead>
<tr>
<td rowspan = 2>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz">Mobilenet_1.0_224(float)</a>
</td>
<td>Pixel 2 </td>
<td>166.5 ms (2.6 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>122.9 ms (1.8 ms) </td>
</tr>
<tr>
<td rowspan = 2>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz)">Mobilenet_1.0_224 (quant)</a>
</td>
<td>Pixel 2 </td>
<td>69.5 ms (0.9 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>78.9 ms (2.2 ms) </td>
</tr>
<tr>
<td rowspan = 2>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz">NASNet mobile</a>
</td>
<td>Pixel 2 </td>
<td>273.8 ms (3.5 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>210.8 ms (4.2 ms)</td>
</tr>
<tr>
<td rowspan = 2>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz">SqueezeNet</a>
</td>
<td>Pixel 2 </td>
<td>234.0 ms (2.1 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>158.0 ms (2.1 ms)</td>
</tr>
<tr>
<td rowspan = 2>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz">Inception_ResNet_V2</a>
</td>
<td>Pixel 2 </td>
<td>2846.0 ms (15.0 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>1973.0 ms (15.0 ms) </td>
</tr>
<tr>
<td rowspan = 2>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz">Inception_V4</a>
</td>
<td>Pixel 2 </td>
<td>3180.0 ms (11.7 ms)</td>
</tr>
<tr>
<td>Pixel xl </td>
<td>2262.0 ms (21.0 ms) </td>
</tr>
</table>
# iOS benchmarks
For running iOS benchmarks, the [benchmark
app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios)
was modified to include the appropriate model and `benchmark_params.json` was
modified to set `num_threads` to 1.
<table>
<thead>
<tr>
<th>Model Name</th>
<th>Device </th>
<th>Mean inference time (std dev)</th>
</tr>
</thead>
<tr>
<td>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz">Mobilenet_1.0_224(float)</a>
</td>
<td>iPhone 8 </td>
<td>32.2 ms (0.8 ms)</td>
</tr>
<tr>
<td>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz)">Mobilenet_1.0_224 (quant)</a>
</td>
<td>iPhone 8 </td>
<td>24.4 ms (0.8 ms)</td>
</tr>
<tr>
<td>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/nasnet_mobile_2018_04_27.tgz">NASNet mobile</a>
</td>
<td>iPhone 8 </td>
<td>60.3 ms (0.6 ms)</td>
</tr>
<tr>
<td>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz">SqueezeNet</a>
</td>
<td>iPhone 8 </td>
<td>44.3 (0.7 ms)</td>
</tr>
<tr>
<td>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz">Inception_ResNet_V2</a>
</td>
<td>iPhone 8</td>
<td>562.4 ms (18.2 ms)</td>
</tr>
<tr>
<td>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz">Inception_V4</a>
</td>
<td>iPhone 8 </td>
<td>661.0 ms (29.2 ms)</td>
</tr>
</table>
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