Course Outline

Introduction

  • 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

Troubleshooting

Summary and Conclusion

Requirements

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

Audience

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

Testimonials (3)

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