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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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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

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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