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You're reading from  TinyML Cookbook - Second Edition

Product typeBook
Published inNov 2023
PublisherPackt
ISBN-139781837637362
Edition2nd Edition
Concepts
Machine Learning
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Author (1)
Gian Marco Iodice
Gian Marco Iodice
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Gian Marco Iodice

Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. The Arm Compute Library is currently the most performant library for ML on Arm, and it's deployed on billions of devices worldwide – from servers to smartphones. Gian Marco holds an MSc degree, with honors, in electronic engineering from the University of Pisa (Italy) and has several years of experience developing ML and computer vision algorithms on edge devices. Now, he's leading the ML performance optimization on Arm Mali GPUs. In 2020, Gian Marco cofounded the TinyML UK meetup group to encourage knowledge-sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.
Read more about Gian Marco Iodice

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Acquiring QQVGA images with the YCbCr422 color format

When compiling the previous sketch for the Arduino Nano, you may have noticed the following warning message in the IDE’s output log: Low memory available, stability may occur. This warning message appears because the QVGA image in the RGB565 color format requires a buffer of 153.6 KB, equivalent to roughly 60% of the available SRAM in the microcontroller.

In this recipe, we will show how to acquire an image at a lower resolution and use the YCbCr422 color format to reduce memory requirements, without compromising image quality.

Getting ready

Images are well known to require big chunks of memory, which might be a problem when dealing with microcontrollers.

Lowering the image resolution is a common practice to reduce the image memory size. Common image resolutions for microcontrollers are smaller than the QVGA (320x240) previously used, such as QQVGA (160x120) or QQQVGA (80x60...

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TinyML Cookbook - Second Edition
Published in: Nov 2023Publisher: PacktISBN-13: 9781837637362
© 2023 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Gian Marco Iodice

Gian Marco Iodice is team and tech lead in the Machine Learning Group at Arm, who co-created the Arm Compute Library in 2017. The Arm Compute Library is currently the most performant library for ML on Arm, and it's deployed on billions of devices worldwide – from servers to smartphones. Gian Marco holds an MSc degree, with honors, in electronic engineering from the University of Pisa (Italy) and has several years of experience developing ML and computer vision algorithms on edge devices. Now, he's leading the ML performance optimization on Arm Mali GPUs. In 2020, Gian Marco cofounded the TinyML UK meetup group to encourage knowledge-sharing, educate, and inspire the next generation of ML developers on tiny and power-efficient devices.
Read more about Gian Marco Iodice

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