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Which is better tensorflow or tensorflow lite for microcontrollers?


Asked by Amy Arellano on Dec 13, 2021 FAQ



TensorFlow Lite for Microcontrollers is designed for the specific constraints of microcontroller development. If you are working on more powerful devices (for example, an embedded Linux device like the Raspberry Pi), the standard TensorFlow Lite framework might be easier to integrate. The following limitations should be considered:
Moreover,
TensorFlow Lite for Microcontrollers is designed to run machine learning models on microcontrollers and other devices with only few kilobytes of memory. The core runtime just fits in 16 KB on an Arm Cortex M3 and can run many basic models. It doesn't require operating system support, any standard C or C++ libraries, or dynamic memory allocation.
Also, TensorFlow Mobile is used for a mobile platform such as iOS and Android. This is for those developers who have a successful TensorFlow model and want to integrate their model into a mobile environment. This is also for those who are not able to use TensorFlow Lite.
One may also ask,
But TensorFlow Lite is a deep learning framework for local inference, specifically for the low computational hardware. It allows on-device machine learning by assisting developers in running their models on compatible hardware and IoT devices.
Next,
TensorFlow Lite for Microcontrollers is designed for the specific constraints of microcontroller development. If you are working on more powerful devices (for example, an embedded Linux device like the Raspberry Pi), the standard TensorFlow Lite framework might be easier to integrate.