raspberry pi: added with guide to tf to hef conversion

wiki/linux/raspberry_pi.md

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+# Raspberry Pi
+
+A [Rapberry Pi](https://www.raspberrypi.com) is a single board comuter.
+
+## Hardware Additions
+
+There are various hardware additions which can be used with the Raspberry Pi.
+This section addresses them.
+
+### AI HAT+
+
+The [AI HAT](https://www.raspberrypi.com/documentation/accessories/ai-hat-plus.html) is an
+extension which uses the Hailo AI module for use with the [Raspberry Pi
+5](https://www.raspberrypi.com/products/raspberry-pi-5).
+
+#### AI HAT+ Usage
+
+This section addresses the usage of the
+[AI HAT](https://www.raspberrypi.com/documentation/accessories/ai-hat-plus.html).
+
+#### Preparing TensorFlow Models for the AI HAT+
+
+For neural networks to run on the Hailo AI module and the AI HAT+ they have to be converted to the
+`.hef` format.
+This section assumes the neural network is using
+[TensorFlow](/wiki/programming_language/python.md#tensorflow) and is available as a `.tf` or
+`.tflite` file.
+
+To convert TensorFlow models first the Hailo 8 Software Suite needs to be downloaded.
+This can be done from the [official website](https://hailo.ai/developer-zone/software-downloads/)
+altough an account is used to be able to download the software.
+
+After downloading, extracting and then navigating into the folder a heavily customized
+[Docker](/wiki/docker.md) container can be started by running the following command.
+However it is recommended to slightly modify this file.
+Add a volume that contains the TensorFlow model, that is to be converted, to the environment
+variable `DOCKER_ARGS` which is set in the file `hailo_ai_sw_suite_docker_run.sh`.
+
+```sh
+./hailo_ai_sw_suite_docker_run.sh
+```
+
+Using the tools which come in this container a `.tf` or `.tflite` model can be converted to the
+`.hef` format.
+
+For this to work run the following commands inside the Docker container.
+The first command takes the path to the tensorflow model (`<path-to-tf-model>`) and will output a
+`.har` model.
+The second command is optional but recommended and takes the path to this `.har` model
+(`<path-to-har-model`) and returns an optimized `.har` model.
+The third and final command compiles the (optimized) `.har` model, which is given as the input, and
+outputs the final `.hre` model, which then can be used with the Hailo AI module.
+
+```sh 
+hailo parser tf <path-to-tf-model>
+hailo optimize --use-random-calib-set <path-to-har-model>
+hailo compiler <path-to-optimized-har-model>
+```