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Work Flow

xmos-ai-tools is available on https://pypi.org/project/xmos-ai-tools/. It includes:

  • the MLIR-based XCore optimizer(xformer) to optimize Tensorflow Lite models for XCore
  • the XCore tflm interpreter to run the transformed models on host

Installation steps

Perform the following steps once

  • Install xmos-ai-tools:

    # Create a virtual environment with
python3 -m venv <name_of_virtualenv>

# Activate the virtual environment
# On Windows, run:
<name_of_virtualenv>\Scripts\activate.bat
# On Linux and MacOS, run:
source <name_of_virtualenv>/bin/activate

# Install xmos-ai-tools from PyPI
pip3 install xmos-ai-tools --upgrade

    Use pip3 install xmos-ai-tools --pre --upgrade instead if you want to install the latest development version.

    Installing xmos-ai-tools will make the xcore-opt binary available in your shell to use directly.

  • Obtain the tool-chain from http://www.xmos.ai/tools and install it according to the platform instructions.

  • Setup XMOS_AITOOLSLIB_PATH environment variable. This is used to identify the installed location of xmos-ai-tools library and headers.

    On Windows, run the following command:

    FOR /F "delims=" %i IN ('python -c "import xmos_ai_tools.xinterpreters.device as device_lib; import os; print(os.path.dirname(device_lib.__file__))"') DO set XMOS_AITOOLSLIB_PATH=%i

    On MacOS and Linux, run the following command:

    export XMOS_AITOOLSLIB_PATH=$(python -c "import xmos_ai_tools.xinterpreters.device as device_lib; import os; print(os.path.dirname(device_lib.__file__))")

Example applications

These are 5 example models; in order of complexity

  • app_no_flash - a single model, no flash memory used. This is the fastest but most pressure on internal memory.
  • app_flash_single_model - a single model, with learned parameters in flash memory. This removes a lot of pressure on internal memory.
  • app_flash_two_models - two models, with learned parameters in flash memory.
  • app_flash_two_models_one_arena - two models, with learned parameters in flash memory. The models share a single tensor arena (scratch memory).

More info regarding the generated C++ model files

The model code is compiled to C++ source and header. The generated header file contains the simple API to interact with the model. Some of the commonly used functions are:

  • model_init(void *flash_data) This takes a single parameter, which is a channel end to the flash server.
  • model_input_ptr(int index) This returns a pointer to the data where the input tensor should be stored; index should be set to zero unless there are multiple inputs.
  • model_invoke() This runs an inference
  • model_output_ptr(int index) This returns a pointer to the data where the output tensor would be stored.

Integration with sensors

There are many sensor interfaces, we will soon publish example programs to interface to PDM microphones and MIPI/SPI cameras