Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • TinyML Cookbook
  • Toc
  • feedback
TinyML Cookbook

TinyML Cookbook

By : Gian Marco Iodice
4.9 (11)
close
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)
close

Designing and training a tiny CIFAR-10 model

The tight memory constraint on LM3S6965 forces us to design a model with extremely low memory utilization. In fact, the target microcontroller has four times less memory capacity than Arduino Nano.

Despite this challenging constraint, in this recipe, we will be leveraging the following tiny model for the CIFAR-10 image classification, capable of running on LM3S6965:

Figure 7.1 – A model tailored for CIFAR-10 dataset image classification

The preceding network will be designed with TF and the Keras API.

The following Colab file (in the Designing and training a tiny CIFAR-10 model section) contains the code referred to in this recipe:

  • prepare_model.ipynb

(https://github.com/PacktPublishing/TinyML-Cookbook/blob/main/Chapter07/ColabNotebooks/prepare_model.ipynb).

Getting ready

The network tailored in this recipe takes inspiration from the success of the MobileNet V1 on the ImageNet...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete