How do I add TFLM to the new Ti platform (Cortex-R5F)

First of all, thank for the resources provided.
I am a researcher trying to use the single chip field for the first time.

After seeing many TensorFlow Lite official documents, a lot of information about Ti is that it only supports the Cortex-M series platform.

Now the hardware I am using is Ti AWR2944 with Cortex-R5F and I use CCS as IDE. How do I import TensorFlow Lite for micro into my project?

[Google DeepMind Assisted]

Integrating TensorFlow Lite for Microcontrollers (TFLM) into a project on a new platform like the TI AWR2944 with a Cortex-R5F core, especially when the primary support is for the Cortex-M series, involves a few key steps. Here’s a simplified guide:

  1. Familiarize Yourself with TFLM: Understand the structure and build process of TFLM. It’s designed to be portable and has minimal dependencies, making it suitable for a variety of microcontroller environments.
  2. Set Up Your Development Environment: Ensure that your Code Composer Studio (CCS) IDE is set up for development with the TI AWR2944 chip. This includes configuring the toolchain for Cortex-R5F development and ensuring you have the necessary SDKs or libraries specific to your hardware.
  3. Adapt TFLM for Cortex-R5F:
  • Processor Architecture: While TFLM is optimized for Cortex-M cores, its portability allows it to be adapted to other architectures like Cortex-R. You might need to make modifications to the TFLM platform layer to accommodate the architectural differences of Cortex-R5F.
  • Hardware Abstraction: Implement any necessary hardware abstraction layers (HAL) to interface TFLM with your specific hardware features, such as timers, I/O, or custom peripherals.
  1. Import TFLM into Your CCS Project:
  • Include TFLM Source: You can clone the TFLM repository and include it in your project, or directly add the TFLM source files to your project structure in CCS.
  • Adjust Build Settings: Configure your project’s build settings to include the TFLM source and header files. Ensure the compiler and linker are set up to handle the TFLM code alongside your existing project code.
  1. Implement Platform-Specific Optimizations: Consider implementing optimizations that leverage the Cortex-R5F features, such as DSP instructions or other hardware accelerations, to improve the performance of TFLM on your device.
  2. Test and Validate: Begin with simple TFLM examples to test the integration. Use these tests to verify that TFLM is functioning correctly on your hardware and to identify any issues that need to be addressed.

Given the novelty of using TFLM with Cortex-R5F through CCS, this process may require significant adaptation and testing. You may also need to engage with the TFLM and TI developer communities for support and potentially contribute back any new adaptations or optimizations that could benefit others working in similar environments.