======= ArmNN TFLite Delegate ======
Khadas Edges and VIMs are powered by **ARM Cortex-A CPUs** and **Mali GPUs**. 

We can make use of the available computation power for running TFLite models using ArmNN Libraries by leveraging [[https://www.tensorflow.org/lite/performance/delegates | TFLite interpreter Delegate]].

The Delegate is provided by ARM as [[gh>arm-software/armnn]] and the provided examples are made to make use of it.

<WRAP info>
This library depends on OpenCL, make sure you are using the following platforms to ensure the driver is present.
^ board ^ Linux Kernel (BSP) ^ OS ^ 
| VIM3 | 4.9 \\ 5.15 | Ubuntu 22.04|
| VIM3L | 4.9 \\ 5.15 | Ubuntu 22.04|
| VIM4| 5.4 \\ 5.15 | Ubuntu 22.04|
| Edge2| 5.10| Ubuntu 22.04|

You can refer to the [[products:sbc:common:applications:opencl|]] doc for more info.
</WRAP>



===== Get source code =====
Clone the examples [[gh>sravansenthiln1/armnn_tflite]]

```shell
$ git clone https://github.com/sravansenthiln1/armnn_tflite
$ cd armnn_tflite
```

===== Setup the environment =====
==== Install pip ====
```shell
$ sudo apt-get install python3-pip
```

==== Install necessary python packages ====
```shell
$ pip3 install numpy pillow
```

==== Install the TFLite runtime interpreter ====
```shell
$ pip3 install --extra-index-url https://google-coral.github.io/py-repo/ tflite_runtime
```

==== Download ArmNN libraries ====
```shell
$ wget -O ArmNN-aarch64.tgz https://github.com/ARM-software/armnn/releases/download/v23.08/ArmNN-linux-aarch64.tar.gz
$ mkdir libs
$ tar -xvf ArmNN-aarch64.tgz -C libs
```

===== Run Examples =====
Taking the Mobilenet v1 as example.

==== Enter the example directory ====
```shell
$ cd mobilenet_v1
```

==== Create library symlinks ====
```shell
$ sudo ln ../libs/libarmnnDelegate.so.29.0 libarmnnDelegate.so.29
$ sudo ln ../libs/libarmnn.so.33.0 libarmnn.so.33
```

==== Run the example ====
```shell
$ python3 run_inference.py
```

<WRAP tip>
You can modify whether you want to use the CPU or GPU to accelerate the inference.
Change the ''BACKEND'' variable in the code to use either ''GpuAcc'' - GPU or ''CpuAcc'' - CPU backends.
</WRAP>