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Pregled bibliografske jedinice broj: 1089990

Inference speed and quantisation of neural networks with TensorFlow Lite for Microcontrollers framework


Đokić, Kristian; Martinović, Marko; Mandušić, Dubravka
Inference speed and quantisation of neural networks with TensorFlow Lite for Microcontrollers framework // Proceedings of 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)
Corfu: IEEE, 2020. str. 1-6 doi:10.1109/SEEDA-CECNSM49515.2020.9221846 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Inference speed and quantisation of neural networks with TensorFlow Lite for Microcontrollers framework

Autori
Đokić, Kristian ; Martinović, Marko ; Mandušić, Dubravka

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) / - Corfu : IEEE, 2020, 1-6

ISBN
978-1-7281-6445-8

Skup
2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)

Mjesto i datum
Corfu, Grčka, 25-27.09.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Propagation speed ; Quantisation ; Arduino ; Microcontrollers ; neural network

Sažetak
In the last few years, microcontrollers became more and more powerful, and many authors have started to use them for different machine learning projects. One of the most popular frameworks for machine learning is TensorFlow, and their authors began to develop this framework for microcontrollers. The goal of this paper is to analyses the full connected neural networks inference speed depending on the number of neurons of one popular microcontroller (Arduino Nano 33 BLE Sense) with simple neural networks implementation, as well as the impact of neural network weights quantisation. We expected a reduction in the size of the model with the selected quantization by four times, which was achieved, but with a large number of neurons in the neural network. TensorFlow Lite for Microcontrollers is used with the Arduino Integrated Development Environment. Neural networks with two hidden layers are used with a different number of neurons.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove:
Agronomski fakultet, Zagreb,
Veleučilište u Požegi,
Sveučilište u Slavonskom Brodu

Profili:

Avatar Url Dubravka Mandušić (autor)

Avatar Url Kristian Đokić (autor)

Citiraj ovu publikaciju

Đokić, Kristian; Martinović, Marko; Mandušić, Dubravka
Inference speed and quantisation of neural networks with TensorFlow Lite for Microcontrollers framework // Proceedings of 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)
Corfu: IEEE, 2020. str. 1-6 doi:10.1109/SEEDA-CECNSM49515.2020.9221846 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Đokić, K., Martinović, M. & Mandušić, D. (2020) Inference speed and quantisation of neural networks with TensorFlow Lite for Microcontrollers framework. U: Proceedings of 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) doi:10.1109/SEEDA-CECNSM49515.2020.9221846.
@article{article, year = {2020}, pages = {1-6}, DOI = {10.1109/SEEDA-CECNSM49515.2020.9221846}, keywords = {Propagation speed, Quantisation, Arduino, Microcontrollers, neural network}, doi = {10.1109/SEEDA-CECNSM49515.2020.9221846}, isbn = {978-1-7281-6445-8}, title = {Inference speed and quantisation of neural networks with TensorFlow Lite for Microcontrollers framework}, keyword = {Propagation speed, Quantisation, Arduino, Microcontrollers, neural network}, publisher = {IEEE}, publisherplace = {Corfu, Gr\v{c}ka} }
@article{article, year = {2020}, pages = {1-6}, DOI = {10.1109/SEEDA-CECNSM49515.2020.9221846}, keywords = {Propagation speed, Quantisation, Arduino, Microcontrollers, neural network}, doi = {10.1109/SEEDA-CECNSM49515.2020.9221846}, isbn = {978-1-7281-6445-8}, title = {Inference speed and quantisation of neural networks with TensorFlow Lite for Microcontrollers framework}, keyword = {Propagation speed, Quantisation, Arduino, Microcontrollers, neural network}, publisher = {IEEE}, publisherplace = {Corfu, Gr\v{c}ka} }

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  • Scopus


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