Course Outline


  • Microcontroller vs Microprocessor
  • Microcontrollers designed for machine learning tasks

Overview of TensorFlow Lite Features

  • On-device machine learning inference
  • Solving network latency
  • Solving power constraints
  • Preserving privacy

Constraints of a Microcontroller

  • Energy consumption and size
  • Processing power, memory, and storage
  • Limited operations

Getting Started

  • Preparing the development environment
  • Running a simple Hello World on the Microcontroller

Creating an Audio Detection System

  • Obtaining a TensorFlow Model
  • Converting the Model to a TensorFlow Lite FlatBuffer

Serializing the Code

  • Converting the FlatBuffer to a C byte array

Working with Microcontroller'ss C++ Libraries

  • Coding the microcontroller
  • Collecting data
  • Running inference on the controller

Verifying the Results

  • Running a unit test to see the end-to-end workflow

Creating an Image Detection System

  • Classifying physical objects from image data
  • Creating TensorFlow model from scratch

Deploying an AI-enabled Device

  • Running inference on a microcontroller in the field


Summary and Conclusion


  • C or C++ programming experience
  • A basic understanding of Python
  • A general understanding of embedded systems


  • Developers
  • Programmers
  • Data scientists with an interest in embedded systems development
  21 Hours


Related Courses

Advanced Embedded Systems Development

  35 hours

Arduino Programming for Beginners

  21 hours

Microcontroller Design

  35 hours

Raspberry Pi for Beginners

  14 hours

ARM Technology

  14 hours

C Programming for Embedded Systems

  21 hours

Embedded Linux Kernel and Driver Development

  14 hours

Introduction to Embedded Computers

  14 hours

Raspberry Pi

  7 hours

Real Time Operating System

  7 hours

Berkeley DB for Developers

  21 hours

Object Oriented Programming with C++

  7 hours

TensorFlow Lite for Embedded Linux

  21 hours

TensorFlow Lite for Android

  21 hours

TensorFlow Lite for iOS

  21 hours