Table Of ContentTinyML Cookbook
Combine artificial intelligence and ultra-low-power
embedded devices to make the world smarter
Gian Marco Iodice
BIRMINGHAM—MUMBAI
TinyML Cookbook
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TinyML is a registered trademark of the TinyML foundation, and is used with permission.
It is because of the source of support from my wife, Eleonora,
during the long nights of writing that I managed to complete this
unique journey. I dedicate this book to her, who believed in this
project from the very beginning.
Foreword
Without a doubt, the tech industry continues to have an ever-increasing impact on our
daily lives. The changes are as rapid as they are constant and are happening all around
us – in our phones, cars, smart speakers, and the micro gadgets we use to improve
efficiency, wellbeing, and connectivity. Machine learning is one of the most transformative
technologies of our age. Businesses, academics, and engineering communities continue
to understand, evolve, and explore the capabilities of this incredible technology, and are
unlocking the greater potential to enable new use cases across many industries.
I am a product manager for machine learning at Arm. In this role, I am at the center of
the ML revolution that is happening in smartphones, the automotive industry, gaming,
AR, VR, and other devices. It is clear to me that there will be ML functionality in every
single electronics device in the near future – from the world's largest supercomputers,
down to the smallest, low-powered microcontrollers. Working in ML has introduced
me to some of the most brilliant and brightest minds in tech – those who challenge the
orthodoxies that exist in traditional industries, ask the tough questions, and unlock new
value through the use of ML.
When I first met Gian Marco, I could barely spell "ML," yet at that time he was already a
veteran in the space. I was astonished by the breadth and depth of his knowledge and his
ability to solve difficult problems. Together with the team at Arm, he has worked to make
Arm Compute Library (ACL) the most performant library available for ML on Arm. The
success of ACL is unrivaled. It's deployed on billions of devices worldwide – from servers
to flagship smartphones, to smart ovens.
When Gian Marco told me he was writing a book on ML, my immediate reaction
was "Which part?" The ML ecosystem is so diverse, with many different technologies,
platforms, and frameworks to consider. At the same time, I knew that he was the right
person for the job due to his extensive knowledge of all aspects of ML. Additionally, Gian
Marco has an amazing way of explaining things in a straightforward and logical manner.
Gian Marco's book demystifies the world of TinyML by guiding us through a series
of practical, real-world examples. Each example is outlined like a recipe, with a clear
and consistent format throughout, providing an easy-to-follow, step-by-step guide.
Beginning with the first principles, he explains the basics of the electronics or software
techniques that will be used in the recipe. The book then introduces the platforms and
technologies used, followed by the ML – where neural network models are developed,
trained, and deployed on the target device. This really is a "soup to nuts" guide. Each
recipe is a little more challenging than the last, and there is a nice mix of established
and nascent technologies. You don't just learn the "how," you also get an understanding
of the "why." When it comes to edge devices, this book really does provide a panoramic
view of the ML space.
Machine learning continues to disrupt all aspects of technology and getting started
is a must for software developers. This book enables quick onboarding through the
use of readily available and inexpensive technologies. Whether you are new to ML or
have some experience, each recipe provides a steady ramp of knowledge and leaves
enough scope for further self-development and experimentation. Whether you use
this book as a guide or a reference, you will develop a strong foundation in ML for
future development. It will empower your team to get new insights and to achieve new
efficiencies, performance improvements, and even new functionality for your products.
– Ronan Naughton
Senior Product Manager for Machine Learning at Arm
Contributors
About the author
Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who
co-created the Arm Compute Library in 2017. The Arm Compute Library is currently
the most performant library for ML on Arm, and it's deployed on billions of devices
worldwide – from servers to smartphones.
Gian Marco holds an MSc degree, with honors, in electronic engineering from the
University of Pisa (Italy) and has several years of experience developing ML and computer
vision algorithms on edge devices. Now, he's leading the ML performance optimization on
Arm Mali GPUs.
In 2020, Gian Marco cofounded the TinyML UK meetup group to encourage
knowledge-sharing, educate, and inspire the next generation of ML developers on
tiny and power-efficient devices.
About the reviewers
Alessandro Grande is a physicist, an engineer, a communicator, and a technology leader
with a visceral passion for connecting and empowering humans to build more efficient
and sustainable technology. Alessandro is the director of product at Edge Impulse and
cofounded the TinyML Meetups in the UK and in Italy. Prior to Edge Impulse, Alessandro
worked at Arm as a developer evangelist and ecosystem manager with a focus on building
the foundations for a smarter and more efficient IoT. He holds a master's degree in nuclear
and electronic physics from the University of Rome, La Sapienza.
Daksh Trehan began his career as a data analyst. His love for data and statistics is
unimaginable. Various statistical techniques introduced him to the world of ML and data
science. While his focus is on being a data analyst, he loves to forecast given data using
ML techniques. He understands the power of data in today's world and constantly tries to
change the world using various ML techniques and his concrete data visualization skills.
He loves to write articles on ML and AI, and these have bagged him more than 100,000
views to date. He has also contributed as an ML consultant to 365 Days as a TikTok
creator, written by Dr. Markus Rach, available publicly on the Amazon e-book store.
Table of Contents
Preface
1
Getting Started with TinyML
Technical requirements 2 Programming microcontrollers 17
Introducing TinyML 2 Memory architecture 20
What is TinyML? 2 Peripherals 21
Why ML on microcontrollers? 3
Presenting Arduino Nano 33
Why run ML locally? 3
BLE Sense and Raspberry Pi Pico 23
The opportunities and challenges for
Setting up Arduino Web Editor,
TinyML 4
TensorFlow, and Edge Impulse 24
Deployment environments for TinyML 5
Getting ready with Arduino Web Editor 25
tinyML Foundation 7
Getting ready with TensorFlow 25
Summary of DL 7
Getting ready with Edge Impulse 27
Deep neural networks 8 How to do it… 27
Convolutional neural networks 9
Running a sketch on Arduino
Quantization 12
Nano and Raspberry Pi Pico 29
Learning the difference
Getting ready 29
between power and energy 13
How to do it… 30
Voltage versus current 13
Power versus energy 15
2
Prototyping with Microcontrollers
Technical requirements 34 How to do it... 36
Code debugging 101 35 There's more 39
Getting ready 35
