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You're reading from  TinyML Cookbook - Second Edition

Product typeBook
Published inNov 2023
PublisherPackt
ISBN-139781837637362
Edition2nd Edition
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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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Implementing the MFCCs feature extraction with the CMSIS-DSP library

The practical use of Q15 fixed-point arithmetic in computing the FFT magnitude provided the foundational knowledge to build functions using fixed-point arithmetic.

In this recipe, we will exploit this knowledge to rewrite the implementation of the MFCCs feature extraction using the Q15 data format.

Getting ready

In the preceding recipe, we learned that the Raspberry Pi Pico, like many other microcontrollers, does not have hardware acceleration for floating-point arithmetic. Despite this limitation, we now know we can leverage the computation with the fixed-point format and rely solely on integer arithmetic.

However, the MFCCs computation can be optimized even further on the Raspberry Pi Pico by exploiting an incredible additional feature offered by this device: the large program memory size.

As we know, the program memory is reserved for storing the program. However, this memory can also be utilized...

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TinyML Cookbook - Second Edition
Published in: Nov 2023Publisher: PacktISBN-13: 9781837637362

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