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TinyML Cookbook

TinyML Cookbook

By : Gian Marco Iodice
4.9 (11)
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TinyML Cookbook

TinyML Cookbook

4.9 (11)
By: Gian Marco Iodice

Overview of this book

This book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you’ll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you’ll cover recipes relating to temperature, humidity, and the three “V” sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you’ll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you’ll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you’ll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.
Table of Contents (10 chapters)
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Installing TVM with Arm Ethos-U support

In the previous recipe, we briefly talked about the Ethos-U55 program, a command stream used to instruct the operations to execute on the microNPU. However, how is the command stream generated? In this chapter, we will be using TVM, a Deep Learning (DL) compiler technology that aims to generate C code from an ML model for a specific target device.

In this recipe, we will learn what TVM is by preparing the development environment that we will use later on in the chapter.

Getting ready

The goal of this recipe is to install the TVM compiler from the source. The installation needs the following prerequisites:

  • CMake 3.5.0 or later
  • C++ compiler with C++14 support (for example, g++ 5 or later)
  • LLVM 4.0 or later
  • Python 3.7 or Python 3.8

Before getting started, we recommend that you have the Python virtual environment (virtualenv) tool installed to create an isolated Python environment. You can refer to Chapter 7, Running...

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