Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)-Reference-Cited by-同舟云学术

Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)

Author:

Zaidi Syed Ali Raza¹^ORCID,Hayajneh Ali M.²^ORCID,Hafeez Maryam³,Ahmed Q. Z.³^ORCID

Affiliation:

1. School of Electronic and Electrical Engineering, University of Leeds, Leeds, U.K.

2. Department of Electrical Engineering, Faculty of Engineering, The Hashemite University, Zarqa, Jordan

3. School of Computing & Engineering, University of Huddersfield, Huddersfield, U.K.

Funder

Engineering and Physical Sciences Research Council

Royal Academy of Engineering, Transforming Systems through Partnership TSP1040

Royal Academy through the Distinguished International Associates

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference26 articles.

1. StrassenNets: Deep learning with a multiplication budget;tschannen;Proc 35th Int Conf Mach Learn,2018

2. McuNet: Tiny deep learning on IoT devices;lin;Proc Adv Neural Inf Process Syst,2020

3. Resource-efficient machine learning in 2 KB RAM for the Internet of Things;kumar;Proc 34th Int Conf Mach Learn,2017

4. Ternary hybrid neural-tree networks for highly constrained IoT applications;gope;Proc Mach Learn Syst (SysML) Conf,2019

5. MicroNets: Neural network architectures for deploying TinyML applications on commodity microcontrollers;banbury;Proc Mach Learn Syst,2021

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