TinyML

Preface

Something about electronics has captured my imagination for as long as I can remember. We’ve learned to dig rocks from the earth, refine them in mysterious ways, and produce a dizzying array of tiny components that we combine—according to arcane laws—to imbue them with some essence of life.

To my eight-year-old mind, a battery, switch, and filament bulb were enchanting enough, let alone the processor inside my family’s home computer. And as the years have passed, I’ve developed some understanding of the principles of electronics and software that make these inventions work. But what has always struck me is the way a system of simple elements can come together to create a subtle and complex thing, and deep learning really takes this to new heights.

One of this book’s examples is a deep learning network that, in some sense, understands how to see. It’s made up of thousands of virtual “neurons,” each of which follows some simple rules and outputs a single number. Alone, each neuron isn’t capable of much, but combined, and—through training—given a spark of human knowledge, they can make sense of our complex world.

There’s some magic in this idea: simple algorithms running on tiny computers made from sand, metal, and plastic can embody a fragment of human understanding. This is the essence of TinyML, a term that Pete coined and will introduce in Chapter 1. In the pages of this book, you’ll find the tools you’ll need to build these things yourself.

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