I was given a presentation on the Introduction to tinyML (Machine learning on edge) and a bit about TensorFlow lite for microcontrollers at Mozilla firefox club VIT webinar.
Intelligent IOT devices • It helps to explore what type of models you can run on small, low-power devices like microcontrollers. • Summarizing and analyzing data at the edge on low power devices
your models to run on embedded systems • It supports Android, IOS, Arduino etc.. • Not only Python you can use C, C++ and JAVA • Pretrained models • Other tools like • CoreML • PyTorch Mobile
learning models on microcontrollers and other devices with only few kilobytes of memory. • TensorFlow Lite for Microcontrollers is written in C++ 11 and requires a 32-bit platform. • The framework is available as an Arduino library
to a TensorFlow Lite model using the TensorFlow Lite converter. • Convert to a C byte array using standard tools to store it in a read-only program memory on device. • Run inference on device using the C++ library and process the results.
limited subset of TensorFlow operations • Support for a limited set of devices • Low-level C++ API requiring manual memory management • On device training is not supported