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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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Toc

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

Continuous inferencing on the Arduino Nano

As you can guess, the application deployment differs on the Arduino Nano and the Raspberry Pi Pico because the devices have different hardware capabilities.

In this recipe, we will show how to implement a continuous keyword application on the Arduino Nano.

The following Arduino sketch contains the code referred to in this recipe:

  • 07_kws_arduino_nano_ble33_sense.ino:

https://github.com/PacktPublishing/TinyML-Cookbook/blob/main/Chapter04/ArduinoSketches/07_kws_arduino_nano_ble33_sense.ino

Getting ready

The application on the Arduino Nano will be based on the nano_ble33_sense_microphone_continuous.cpp example provided by Edge Impulse, which implements a real-time KWS application. Before changing the code, we want to examine how this example works to get ready for the recipe.

Learning how a real-time KWS application works

A real-time KWS application—for example, the one used in the smart assistant—...

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