Jul 9, 2022 · TensorFlow Lite Micro/TinyML: TensorFlow Lite for Microcontrollers is a library designed to run machine learning models on microcontrollers and other devices with only a few kilobytes of memory. It doesn't require operating system support, standard C or C++ libraries, or dynamic memory allocation.

This example is designed to demonstrate the absolute basics of using TensorFlow Lite for Microcontrollers. It includes the full end-to-end workflow of training a model, converting it for use with TensorFlow Lite for Microcontrollers for running inference on a microcontroller.

Learn how to utilize TensorFlow Lite on microcontrollers to implement AI models. This tutorial covers key concepts, tools, and practical examples.

Nov 22, 2024 · In this tutorial, we’ll walk you through the process of implementing TensorFlow Lite on Microcontrollers, covering the technical background, implementation guide, code examples, best practices, testing and debugging, and more.

Dec 6, 2022 · TensorFlow Lite for Microcontrollers (TFL4uC) is an open source C++ framework by Google supporting developers to generate code for specific microcontrollers from Tensorflow (TF) graphs.

Jul 6, 2020 · This tutorial will show you how to generate source code files in TensorFlow Lite that you can use as a library in any microcontroller build system (Arduino, make, Eclipse, etc.). However, I am going to specifically show you how to include this library in STM32CubeIDE.

Use an efficient processor and a rich software environment! Loop body 1. Original code. 3. Zero-overhead loop – reduce loop size. 2. Vectorize MAC – reduce # loops. 4. XY & AGUs – eliminate LDs and increments Single instruction loop. AGU registers allow: Implicit load, unpack, sign extension, store & address pointer updates...all in a single cycle!

May 8, 2017 · I gave a talk last week at the Embedded Vision Summit, and one question that came up was how to run neural networks trained in TensorFlow on tiny, power-efficient CPUs like the EFM32. This microcontroller is based on an ARM M4 design, and uses about 2.5 milliwatts when running at 40 MHz.

Oct 25, 2024 · TensorFlow Lite for Microcontrollers is the answer! From figuring out which microcontrollers support TensorFlow Lite to deploying a trained AI model on Arduino, ESP32, and other platforms, this article will teach you how to use TensorFlow Lite to apply machine learning on microcontrollers.

Apr 24, 2023 · This blog details the inner workings of TensorFlow Lite for Microcontrollers and the role of Flatbuffers in them.