Recent advances in state-of-the-art ultra-low power embedded devices for machine learning (ML) have permitted a new class of products whose key features enable ML capabilities on microcontrollers with less than 1 mW power consumption (TinyML). TinyML provides a unique solution by aggregating and analyzing data at the edge on low-power embedded devices. However, we have only recently been able to run ML on microcontrollers, and the field is still in its infancy, which means that hardware, software, and research are changing extremely rapidly. Consequently, many TinyML frameworks have been developed for different platforms to facilitate the deployment of ML models and standardize the process. Therefore, in this paper, we focus on benchmarking two popular frameworks: Tensorflow Lite Micro (TFLM) on the Arduino Nano BLE and CUBE AI on the STM32-NucleoF401RE to provide a standardized framework selection criterion for specific applications.

TinyML Platforms Benchmarking / Osman, Anas; Abid, Usman; Gemma, Luca; Perotto, Matteo; Brunelli, Davide. - ELETTRONICO. - 866:(2022), pp. 139-148. (Intervento presentato al convegno International Conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2021 tenutosi a Pisa; online nel 21st-22nd September 2021) [10.1007/978-3-030-95498-7_20].

TinyML Platforms Benchmarking

Gemma, Luca
;
Brunelli, Davide
Ultimo
2022-01-01

Abstract

Recent advances in state-of-the-art ultra-low power embedded devices for machine learning (ML) have permitted a new class of products whose key features enable ML capabilities on microcontrollers with less than 1 mW power consumption (TinyML). TinyML provides a unique solution by aggregating and analyzing data at the edge on low-power embedded devices. However, we have only recently been able to run ML on microcontrollers, and the field is still in its infancy, which means that hardware, software, and research are changing extremely rapidly. Consequently, many TinyML frameworks have been developed for different platforms to facilitate the deployment of ML models and standardize the process. Therefore, in this paper, we focus on benchmarking two popular frameworks: Tensorflow Lite Micro (TFLM) on the Arduino Nano BLE and CUBE AI on the STM32-NucleoF401RE to provide a standardized framework selection criterion for specific applications.
2022
Applications in Electronics Pervading Industry, Environment and Society: APPLEPIES 2021
Saponara, S.; De Gloria, A.
Cham, CH
Springer
978-3-030-95497-0
978-3-030-95498-7
Osman, Anas; Abid, Usman; Gemma, Luca; Perotto, Matteo; Brunelli, Davide
TinyML Platforms Benchmarking / Osman, Anas; Abid, Usman; Gemma, Luca; Perotto, Matteo; Brunelli, Davide. - ELETTRONICO. - 866:(2022), pp. 139-148. (Intervento presentato al convegno International Conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2021 tenutosi a Pisa; online nel 21st-22nd September 2021) [10.1007/978-3-030-95498-7_20].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/342107
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