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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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Deploying the model on the Arduino Nano

Now that we have acquired the knowledge of code generation with TVM, we are prepared to shift our attention toward deploying an actual model on physical microcontrollers.

Thus, in this recipe, we aim to deploy the quantized CIFAR-10 model on the Arduino Nano.

Getting ready

To get ready with this recipe, we need to know how to generate and structure an Arduino project.

In the previous recipe, we executed the CIFAR-10 model on the host machine through the Python host-driven interface. However, we haven’t seen any actual code generated apart from a few Python objects returned by TVM.

As mentioned earlier, when dealing with microcontrollers, the output of TVM is a TAR package that contains the C code for the TVM runtime and TVM Lib, known as MLF. The TAR file is created when we call the tvm.micro.generate_project() function and is automatically decompressed, integrating only the necessary files into the target template...

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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