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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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Index

Symbols

16-bit fixed-point (Q15) arithmetic 243

A

accelerometer 409

basic principles 422-425

accelerometer data

acquiring 421-430

accuracy

evaluating, of quantized model 479-483

accuracy of quantized model

evaluating, on test dataset 279-282

activations 8

Adafruit Unified Sensor 128

ADC pin

used, for connecting microphone 199-203

ahead-of-time (AoT) 501

Alif Semiconductor Ensemble

reference link 545

alpha value 383

amperes (A) 14

analog ground (AGND) 202

analog signal 23

analog-to-digital converter (ADC) 23, 197

programming, with Raspberry Pi Pico SDK 205-210

used, for recording audio samples 204

anomaly detection 595

anti-lock braking system (ABS) 19

AoT executor

used, for deploying model with TVM on host machine 511-524

versus graph executor 515

Apache TVM 501

Apollo3 microcontroller 25

Arduino

URL 24

Arduino API

reference link 205

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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