Last edited 2 weeks ago

How to compile model and run inference on Coral Edge TPU

Applicable for STM32MP13x lines, STM32MP15x lines, STM32MP25x lines

1. Article purpose[edit source]

This article aims at describing how to run an inference on Coral Edge TPU using STM32MPUs platforms.

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This article provides a simple example. Other methods exist that might be better adapted to your development constraints. Feel free to explore them.

2. Prerequisites[edit source]

2.1. Installing the Edge TPU compiler[edit source]

To perform an inference on the Coral Edge TPU hardware, we need to convert our TensorFlow Lite model into an Edge TPU model. This can be achieved by using the Edge TPU compiler. To do this, install the Edge TPU compiler on your host computer.

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The Edge TPU compiler can be installed on an host computer. You need to ensure that your host computer uses a Debian-based Linux system.

Install the Coral Edge TPU compiler by running the following commands on your host computer:

  curl | sudo apt-key add -
  echo "deb coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list
  sudo apt-get update
  sudo apt-get install edgetpu-compiler

Enter the following command to check that the compiler has been installed:

  edgetpu_compiler -v 
 Edge TPU Compiler version x.y.z

3. Fetching a Coral Edge TPU compiled model[edit source]

3.1. Coral compiled models[edit source]

Coral [1] offers a wide set of quantized and compiled models for demonstration purposes. Ready-to-use models are available from Coral model zoo.

3.2. Compiling your own model[edit source]

The Edge TPU compiler role is to convert one or several TensorFlow Lite models into Edge TPU compatible models. It takes as an argument your .tflite model and returns a .tflite Edge TPU compatible model. You can pass multiple models as arguments (each separated with a space). They are then co-compiled and ready to share the Edge TPU 8 Mbytes of RAM for parameter data caching.

Be aware that not all the operations supported on TensorFlow Lite are supported on Edge TPU. While building your own model architecture, check the operations and the layers supported by the Edge TPU compiler on Coral AI supported operations .

If your model architecture uses unsupported operations and does not meet all the requirements, then only the first portion of the model will be executed on the Edge TPU compiler. Starting from the first node in your model graph where an unsupported operation occurs, all operations are run on the CPU of the target board even if an Edge TPU supported operation occurs. The Edge TPU compiler cannot partition the model more than once.

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If an important percentage of your model is executed on the CPU, you have to expect a significant degradation of the inference speed compared to a model that is entirely executed on the Edge TPU compiler. To achieve a maximal speed, use only Edge TPU supported operations in your model.

To compile your .tflite model, execute the following command:

  edgetpu_compiler -m 13 your_model_1.tflite 
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  • If you do not specify the -out_dir option, the compiled model is saved in the current directory with the input_filename_edgetpu.tflite name. Another .log file that provides information about data caching and memory consumption is stored in the Edge TPU RAM.
  • The -m option is used to specify the lowest Edge TPU runtime version you want the model to be compatible with. In our case the version 13 is used. Please keep in mind that models are always forward-compatible with newer Edge TPU runtime versions.

3.3. Example: compiling an object detection model[edit source]

The object detection model used is the ssd_mobilenet_v1_coco_quant.tflite downloaded from the Coral [1] website and compiled for the Coral Edge TPU using the steps detailed below:

  unzip ./
  Archive:  ./
            inflating: detect.tflite           
            inflating: labelmap.txt  

Now that the model has been downloaded and extracted successfully, it is time to compile it using the Edge TPU compiler on your host PC:

  edgetpu_compiler -m 13 detect.tflite

3.4. Sending your model to the target[edit source]

In your board workspace directory, create two main directories to organize the workflow:

 cd /usr/local && mkdir -p workspace
 cd /usr/local/workspace && mkdir -p models

Then transfer your compiled model from the host computer to the models directory in the board workspace:

 scp path/to/your/compiled_model_edgetpu.tflite root@<board_ip_address>:/usr/local/workspace/models/

Now that your workspace is ready with the compiled model file, it is time to see how to run an inference using the C++ benchmark application.

4. Running the inference[edit source]

4.1. Installing the benchmark application[edit source]

Configure the AI OpenSTLinux package, then install X-LINUX-AI components for this application:

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The software package is provided AS IS, and by downloading it, you agree to be bound to the terms of the software license agreement (SLA0048). The detailed content licenses can be found here.
 x-linux-ai -i coral-edgetpu-benchmark

4.2. Execute the benchmark on the model[edit source]

Now that our compiled model is loaded on the board, it is time to run an inference using the benchmark example. This example aims at measuring your model average inference time on a predefined number of 25 loops. To do this, execute the following command:

 cd /usr/local/bin/coral-edgetpu-*/tools/
 ./coral_edgetpu_benchmark -m /usr/local/workspace/models/your_compiled_model.tflite -l 25

The first inference may take longer since the model is being loaded on the Coral Edge TPU RAM. This time is not taken into account in the average inference time.

4.3. Customizing your application[edit source]

You can adapt your application to your development constraints and requirements.

To run your application using C/C++ or Python, go through Image classification example.

5. References[edit source]