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TinyML Cookbook
TinyML Cookbook

TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems , Second Edition

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TinyML Cookbook

Unleashing Your Creativity with Microcontrollers

Bringing machine learning (ML) to life on microcontrollers is a thrilling adventure because our creations can go beyond our computers’ boundaries and make an impact in the real world. However, before diving into this fascinating world, let’s take a moment to explore how to craft basic applications on microcontrollers to get up to speed with the principles of embedded programming.

In this chapter, we will start our exploration by handling data transmission over the serial communication protocol, equipping ourselves with a foundation for basic code debugging. The transmitted data will be captured in a log file and uploaded to our cloud storage in Google Drive.

Afterward, we will delve into programming the GPIO peripheral using the Arm Mbed API and use a solderless breadboard to connect external components, such as LEDs and push-buttons.

The aim of this chapter is to delve into the basic principles of microcontroller...

Technical requirements

To complete all the practical recipes of this chapter, we will need the following:

  • An Arduino Nano 33 BLE Sense
  • A Raspberry Pi Pico
  • A SparkFun RedBoard Artemis Nano (optional)
  • A micro-USB data cable
  • A USB-C data cable (optional)
  • 1 x half-size solderless breadboard (30 rows and 10 columns)
  • 1 x red LED
  • 1 x 220 Ω resistor
  • 1 x push-button
  • 5 x jumper wires
  • Laptop/PC with either Linux, macOS, or Windows
  • Google Drive account

The source code and additional material are available in the Chapter02 folder of the GitHub repository: https://github.com/PacktPublishing/TinyML-Cookbook_2E/tree/main/Chapter02.

Transmitting data over serial communication

Code debugging is a fundamental process of software development to identify errors in code.

This recipe will demonstrate how to conduct print debugging on the Arduino Nano and Raspberry Pi Pico by transmitting the following strings to the serial terminal:

  • Initialization completed: once the serial port on the microcontroller has finished initializing
  • Executed: after every 2 seconds of program execution

Getting ready

All programs are prone to bugs, and print debugging is a basic process that displays statements on the output terminal, providing valuable insight into the program’s execution, as shown in the following example:

int func (int func_type, int a) {
  int ret_val = 0;
  switch(func_type){
    case 0:
      printf("FUNC0\n");
      ret_val = func0(a)
      break;
    default:
      printf("FUNC1\n");
      ret_val = func1(a);
  }
  return ret_val;
}

In the preceding...

Reading serial data and uploading files to Google Drive with Python

When developing tinyML projects, our microcontrollers can use serial communication to transfer data of any type to our computer.

In this recipe, we will showcase how to develop a local Python script to retrieve the transmitted data from the PC’s serial port. The program will record one minute of data transmission to a file, which will be uploaded to Google Drive.

Getting ready

Throughout the book, the microcontroller will use serial communication for various scopes, such as:

  • Tracking events when the program runs
  • Debugging sensor functionalities
  • Gathering relevant data for building the dataset used to train and test an ML model

The last point will likely be the most enjoyable. For instance, you will use the microphone to record your vocals or musical recordings and a camera to snap pictures. However, unlike our standard computers or laptops, which have an operating...

There’s more…

In this recipe, we learned how to use the PyDrive library to upload data captured with the microcontroller to the cloud automatically.

PyDrive is not restricted to uploading files to Google Drive only. In reality, PyDrive can carry out the usual tasks that are done in the web browser, such as:

  • Creating files
  • Downloading files
  • Searching for files
  • Delegating files

For more information, refer to the official PyDrive documentation at https://pythonhosted.org/PyDrive/.

For example, you may consider extending the Python script to create a directory in Google Drive to automate uploading files in the cloud fully.

Serial communication is undoubtedly an easy way to get information during the program execution. However, its relatively slow data transfer speed could make it unsuitable for some applications.

The upcoming recipe will show an alternative approach that can only display simple information to the user...

Implementing an LED status indicator on the breadboard

Microcontrollers enable us to interact with the world around us by using sensors and performing physical actions, such as turning an LED on and off or moving an actuator.

In this recipe, we will learn how to connect external components with the microcontroller by building the following electronic circuit on the breadboard:

Figure 2.20: The LED power status indicator circuit

The electronic circuit illustrated in Figure 2.20 uses the red LED to indicate whether the microcontroller is connected to the power source.

Getting ready

Connecting external components to the microcontroller means physically joining two or more metal connectors. Although we could solder these connectors, it is not usual for prototyping because it is not quick and straightforward.

Therefore, this recipe presents a solderless alternative to connect our components effortlessly.

Making contact directly with the microcontroller...

Controlling an external LED with the GPIO

Nowadays, LEDs are everywhere, particularly in our houses, because they use less energy than older lights for the same luminous intensity. However, the LEDs considered for our experiments are not light bulbs but through-hole LEDs for rapid prototyping on the breadboard.

In this recipe, we will discover how to build a basic circuit with an external LED and program the GPIO peripheral to control its light.

Getting ready

To implement this recipe, we need to know how the LED can emit light and how to program the microcontroller GPIO peripheral to turn the light on and off.

Let’s start by explaining what an LED is and how it works.

Understanding the LED functionality

LED stands for Light Emitting Diode and is a semiconductor component that emits light when a current flows through it.

In this book, we will use through-hole LEDs, which are made of the following:

  • A head of transparent material from where...

Turning an LED on and off with a push-button

In contrast to a PC, where the keyboard, mouse, or touchscreen facilitates human interactions with software applications, the physical button represents the most common way to interact with a microcontroller.

In this recipe, we will learn how to integrate a push-button into the electronic circuit built in the previous recipe. Then, we will employ the GPIO peripheral to detect whether the button is pushed or released and use this information to control the LED light.

Getting ready

Before diving into the practical part of this recipe, let’s start by introducing the operating principles of the push-button.

The operating principles of the push-button

From an electronics point of view, a push-button is a device that makes (a.k.a. shorts) or breaks (a.k.a. opens) the connection between two wires. When we press the button, we connect the wires through a mechanical system, allowing the current to flow. However, unlike...

Using interrupts to read the push-button state

The previous recipe showed how to read digital signals with the GPIO peripheral. However, the proposed solution is inefficient because the CPU wastes clock cycles waiting for the button to be pressed while it could perform other tasks in the meantime. Furthermore, this could be a situation where we would keep the CPU in low-power mode when there are no other tasks to run.

Therefore, this recipe will show you how to change the sketch developed in the previous recipe to read the push-button state efficiently using interrupts.

Getting ready

Let’s prepare this recipe by learning what an interrupt is and which Mbed OS API we can use to read the push-button efficiently.

Working with interrupts using the Mbed OS API

An interrupt is a signal that temporarily pauses the main program to address an event through a dedicated function known as an interrupt handler or interrupt service routine (ISR). When the ISR ends the...

Summary

The recipes presented in this chapter covered the basic principles of microcontroller programming, a prerequisite for the projects developed in the rest of this book.

In the first part, we learned how to use the microcontroller to transmit data serially to the computer for generating files to upload to Google Drive.

Then, our focus shifted to the principles of controlling the LED light through the GPIO peripheral. These recipes taught us how to build electronic circuits on the breadboard, determine the appropriate resistor based on the LED emitting light color, and program the GPIO peripheral to output digital signals.

Finally, we discovered how to attach a push-button to the microcontroller and program the GPIO peripheral to read its state. Upon completing this chapter, you should be well prepared to delve into developing your first tinyML project.

In the next chapter, we will implement a basic weather station to predict the occurrence of snowfall using the...

Learn more on Discord

To join the Discord community for this book – where you can share feedback, ask questions to the author, and learn about new releases – follow the QR code below:

https://packt.link/tiny

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Key benefits

  • Over 20+ new recipes, including recognizing music genres and detecting objects in a scene
  • Create practical examples using TensorFlow Lite for Microcontrollers, Edge Impulse, and more
  • Explore cutting-edge technologies, such as on-device training for updating models without data leaving the device

Description

Discover the incredible world of tiny Machine Learning (tinyML) and create smart projects using real-world data sensors with the Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano. TinyML Cookbook, Second Edition, will show you how to build unique end-to-end ML applications using temperature, humidity, vision, audio, and accelerometer sensors in different scenarios. These projects will equip you with the knowledge and skills to bring intelligence to microcontrollers. You'll train custom models from weather prediction to real-time speech recognition using TensorFlow and Edge Impulse.Expert tips will help you squeeze ML models into tight memory budgets and accelerate performance using CMSIS-DSP. This improved edition includes new recipes featuring an LSTM neural network to recognize music genres and the Faster-Objects-More-Objects (FOMO) algorithm for detecting objects in a scene. Furthermore, you’ll work on scikit-learn model deployment on microcontrollers, implement on-device training, and deploy a model using microTVM, including on a microNPU. This beginner-friendly and comprehensive book will help you stay up to date with the latest developments in the tinyML community and give you the knowledge to build unique projects with microcontrollers!

Who is this book for?

This book is ideal for machine learning engineers or data scientists looking to build embedded/edge ML applications and IoT developers who want to add machine learning capabilities to their devices. If you’re an engineer, student, or hobbyist interested in exploring tinyML, then this book is your perfect companion. Basic familiarity with C/C++ and Python programming is a prerequisite; however, no prior knowledge of microcontrollers is necessary to get started with this book.

What you will learn

  • Understand the microcontroller programming fundamentals
  • Work with real-world sensors, such as the microphone, camera, and accelerometer
  • Implement an app that responds to human voice or recognizes music genres
  • Leverage transfer learning with FOMO and Keras
  • Learn best practices on how to use the CMSIS-DSP library
  • Create a gesture-recognition app to build a remote control
  • Design a CIFAR-10 model for memory-constrained microcontrollers
  • Train a neural network on microcontrollers

Product Details

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Publication date : Nov 29, 2023
Length: 664 pages
Edition : 2nd
Language : English
ISBN-13 : 9781837633968
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Product Details

Publication date : Nov 29, 2023
Length: 664 pages
Edition : 2nd
Language : English
ISBN-13 : 9781837633968
Vendor :
Google
Category :
Languages :

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Table of Contents

15 Chapters
Getting Ready to Unlock ML on Microcontrollers Chevron down icon Chevron up icon
Unleashing Your Creativity with Microcontrollers Chevron down icon Chevron up icon
Building a Weather Station with TensorFlow Lite for Microcontrollers Chevron down icon Chevron up icon
Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands Chevron down icon Chevron up icon
Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 1 Chevron down icon Chevron up icon
Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 2 Chevron down icon Chevron up icon
Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico Chevron down icon Chevron up icon
Classifying Desk Objects with TensorFlow and the Arduino Nano Chevron down icon Chevron up icon
Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico Chevron down icon Chevron up icon
Deploying a CIFAR-10 Model for Memory-Constrained Devices with the Zephyr OS on QEMU Chevron down icon Chevron up icon
Running ML Models on Arduino and the Arm Ethos-U55 microNPU Using Apache TVM Chevron down icon Chevron up icon
Enabling Compelling tinyML Solutions with On-Device Learning and scikit-learn on the Arduino Nano and Raspberry Pi Pico Chevron down icon Chevron up icon
Conclusion Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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Kam F Siu Jan 30, 2024
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N/A Jan 29, 2024
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Je trouve que le contenu du livre est claire concis et pas du tout compliqué e merci beaucoup...
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Jean Labbe Sep 09, 2024
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Well Donne! Excellent for beginners. Explanations are clear and easy to follow. Illustrations are very useful with all steps.
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Mark D Dec 01, 2023
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Having read the first edition of this book that I own, I received a pre-release copy of the second edition from <PACKT> to review for this book. I was a co-editor on another <PACKT> book related to RTOS (Real-time Operating Systems) so I get pre-release copies from time to time to review.This book is a great expansion of the first edition and includes more visual diagrams and expanded detail to explain hardware connectivity, MEMS sensors and how they operate, different types of machine learning inference with sensor devices, the Edge Impulse cloud-based no-code machine learning toolkit, and Tensorflow programming using the Arduino IDE.I wouldn't consider this book for absolute beginners but a beginner would need to read it a couple of times first to understand core concepts before trying to do the "How To Do It" sections at the end of each example project. This book is more suited with someone who has some exposure to embedded microcontroller programming with Arduino IDE, Arduino dev boards like the Nano 33 BLE Sense, the Raspberry Pi Pico dev board, and perhaps the ESP32 dev board variants from Espressif Systems.The new Arduino Nano 33 BLE Sense 2 has recently come out and should apply to this book as well for the Edge Impulse and Tensorflow chapters for deploying TinyML machine learning models. If you buy this book now and buy an Arduino Nano 33 BLE Sense dev board and peripherals for Christmas, you can have enough time to read the book and deploy TinyML models over the Christmas holidays after your dev board arrives!I work with embedded machine learning on intelligent wireless IoT devices for my business and can deploy TinyML models to almost any ARM Cortex-M embedded microcontroller out there. I use other machine learning tools to deploy TinyML models directly onto MEMS sensors as well.Gian Marco Iodice is an expert in the field of embedded machine learning due to his work at ARM in the UK and his education experience in researching the field of TinyML on embedded systems or resource-constrained embedded devices for computer vision. The principles of this book cover a wide range of TinyML possibilities with great examples from deploying machine learning models from scratch using the Arduino IDE with C and C++ code and ARM MBED OS to no-code tools like Edge Impulse.For anyone wanting to learn how to deploy machine learning models to an embedded microcontroller development kit like the Arduino Nano 33 Ble Sense or the Raspberry Pi Pico dev kit, you must get this book to learn how to do it easily while learning important concepts at the same time. You can also join the "Embedded Systems Professionals" Discord channel to ask the author of the book, Gian Mardo Iodice, questions about the contents of the book and to get some help on how to deploy TinyML models to your dev board.To conclude, I know you will enjoy the book as much as I did. The second edition is an improvement to the first edition with updated code fixes, more diagrams, expanded explanations of topics, and updated information. Buy an Arduino Nano 33 BLE Sense dev board, buy some peripheral sensors to connect to your Arduino dev board, and start deploying TinyML models with the "TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems" today. I highly recommend this book if you want to learn about the future of machine learning on embedded devices and how to actually deploy TinyML models onto embedded systems to make those systems really smart.
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Heena Chouhan Feb 07, 2024
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If you're into microcontrollers and machine learning like I am, this book is an absolute gem. It's the perfect fusion of both worlds, providing valuable insights on how to leverage machine learning to tackle real-world challenges on power and compute-constrained devices.
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