TensorFlow Lite helps in designing and running TinyML models across a wide range of low-power hardware devices. Do you know yet? Try and experience it right in this article.
TensorFlow Lite has emerged as a popular platform for running machine learning models on the edge. A microcontroller is a tiny low-cost device to perform the specific tasks of embedded systems.
In a workshop held as part of Google I/O, TensorFlow founding member Pete Warden delved deep into the potential use cases of TensorFlow Lite for microcontrollers.
Further, quoting the definition of TinyML from a blog, he said:
“Tiny machine learning is capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of ways-on-use-case and targeting battery operated devices.”
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