TF-lite build in custom Software Development Kit (SDK)

TensorFlow General Discussion
1

Hello,

I am working to integrate TFlite-micro to my SDK that is ARM Cortex M33 based (in Eclipse environment for Renesas DA1469x family). Currently, I am getting a lot of errors, mostly using the gcc_embedded staff for the “flatbuffers” and “ruy”.

Are there any intructions or previous experience in this kind of integration?

Thank you
Giorgos

3

Hey Giorgos,

Integrating TFLite-micro into a custom SDK, especially for an ARM Cortex M33-based platform, can be tricky due to dependencies like flatbuffers and ruy. You might want to check if your toolchain is fully compatible with these libraries. Also, some software development companies specialize in embedded AI solutions and could offer insights or even prebuilt adaptations for Renesas DA1469x. Have you tried looking into any open-source implementations for similar microcontrollers?

Hope this helps!

5

Integrating TensorFlow Lite (TF-Lite) into a Custom Software Development Kit (SDK) can be a powerful way to bring machine learning capabilities to mobile and edge devices. TF-Lite is optimized for on-device inference, making it lightweight and fast, which is ideal for custom SDKs targeting Android, iOS, or embedded platforms.

When building TF-Lite into a custom SDK, there are a few key considerations:

  • Model conversion: Ensure your TensorFlow model is converted to .tflite format using the TF Lite Converter.
  • Interpreter integration: Your SDK should wrap the TF Lite Interpreter API to make it easier for end-users to load models and run inferences.
  • Cross-platform support: If the SDK needs to support multiple platforms, you’ll need to manage platform-specific bindings (e.g., JNI for Android, Objective-C/Swift for iOS).
  • Dependency management: Consider how you’ll bundle the TF-Lite runtime within your SDK or allow developers to manage dependencies externally.

This approach is common in applications like image recognition, natural language processing, or predictive analytics that need to run offline or with low latency.

If you’re exploring this as part of a broader product or solution, you might find this overview of Custom Software Development Services "Modified by moderator"helpful to understand how teams structure such SDK development with ML integration.

Hope this gives some clarity on how TF-Lite can be effectively embedded into a custom SDK!