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TinyML Cookbook

You're reading from   TinyML Cookbook Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801814973
Length 344 pages
Edition 1st Edition
Tools
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Author (1):
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Gian Marco Iodice Gian Marco Iodice
Author Profile Icon Gian Marco Iodice
Gian Marco Iodice
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Toc

Table of Contents (10) Chapters Close

Preface 1. Chapter 1: Getting Started with TinyML 2. Chapter 2: Prototyping with Microcontrollers FREE CHAPTER 3. Chapter 3: Building a Weather Station with TensorFlow Lite for Microcontrollers 4. Chapter 4: Voice Controlling LEDs with Edge Impulse 5. Chapter 5: Indoor Scene Classification with TensorFlow Lite for Microcontrollers and the Arduino Nano 6. Chapter 6: Building a Gesture-Based Interface for YouTube Playback 7. Chapter 7: Running a Tiny CIFAR-10 Model on a Virtual Platform with the Zephyr OS 8. Chapter 8: Toward the Next TinyML Generation with microNPU 9. Other Books You May Enjoy

Communicating with the MPU-6050 IMU through I2C

The dataset is the core part of any ML project because it has implications regarding the model's performance. However, recording sensor data is often a challenging task in TinyML since it requires low-level interfacing with the hardware.

In this recipe, we will use the MPU-6050 Inertial Measurement Unit (IMU) to teach the fundamentals behind a common communication protocol for sensors: the Inter-Integrated Circuit (I2C). By the end of this recipe, we will have an Arduino sketch to read out the MPU-6050 address.

The following Arduino sketch contains the code that will be referred to in this recipe

  • 01_i2c_imu_addr.ino:

https://github.com/PacktPublishing/TinyML-Cookbook/blob/main/Chapter06/ArduinoSketches/01_i2c_imu_addr.ino.

Getting ready

For this recipe, we need to know what an IMU sensor is and how to retrieve its measurements with the I2C communication protocol.

The IMU sensor is an electronic device...

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