Tinyml benchmark: Executing fully connected neural networks on commodity microcontrollers
B Sudharsan, S Salerno, DD Nguyen… - 2021 IEEE 7th World …, 2021 - ieeexplore.ieee.org
Recent advancements in the field of ultra-low-power machine learning (TinyML) promises to
unlock an entirely new class of edge applications. However, continued progress is …
unlock an entirely new class of edge applications. However, continued progress is …
Benchmarking tinyml systems: Challenges and direction
CR Banbury, VJ Reddi, M Lam, W Fu, A Fazel… - arXiv preprint arXiv …, 2020 - arxiv.org
Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to
unlock an entirely new class of smart applications. However, continued progress is limited …
unlock an entirely new class of smart applications. However, continued progress is limited …
Enabling large neural networks on tiny microcontrollers with swapping
Running neural networks (NNs) on microcontroller units (MCUs) is becoming increasingly
important, but is very difficult due to the tiny SRAM size of MCU. Prior work proposes many …
important, but is very difficult due to the tiny SRAM size of MCU. Prior work proposes many …
Tinyml platforms benchmarking
A Osman, U Abid, L Gemma, M Perotto… - … on Applications in …, 2021 - Springer
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 …
(ML) have permitted a new class of products whose key features enable ML capabilities on …
Tinyol: Tinyml with online-learning on microcontrollers
Tiny machine learning (TinyML) is a fast-growing research area committed to democratizing
deep learning for all-pervasive microcontrollers (MCUs). Challenged by the constraints on …
deep learning for all-pervasive microcontrollers (MCUs). Challenged by the constraints on …
TinyML for ubiquitous edge AI
S Soro - arXiv preprint arXiv:2102.01255, 2021 - arxiv.org
TinyML is a fast-growing multidisciplinary field at the intersection of machine learning,
hardware, and software, that focuses on enabling deep learning algorithms on embedded …
hardware, and software, that focuses on enabling deep learning algorithms on embedded …
Deep learning on microcontrollers: A study on deployment costs and challenges
F Svoboda, J Fernandez-Marques, E Liberis… - Proceedings of the 2nd …, 2022 - dl.acm.org
Microcontrollers are an attractive deployment target due to their low cost, modest power
usage and abundance in the wild. However, deploying models to such hardware is non …
usage and abundance in the wild. However, deploying models to such hardware is non …
Survey and comparison of milliwatts micro controllers for tiny machine learning at the edge
M Giordano, L Piccinelli… - 2022 IEEE 4th International …, 2022 - ieeexplore.ieee.org
Low power Internet of Things devices are growing in number and computational capabilities,
pushing to ubiquitous deployment of smart sensors that embed on board both the sensing …
pushing to ubiquitous deployment of smart sensors that embed on board both the sensing …
TinyML: A systematic review and synthesis of existing research
Tiny Machine Learning (TinyML), a rapidly evolving edge computing concept that links
embedded systems (hardware and software) and machine learning, with the purpose of …
embedded systems (hardware and software) and machine learning, with the purpose of …
Reduced precision floating-point optimization for Deep Neural Network On-Device Learning on microcontrollers
Abstract Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller Units
(MCUs) is a key step for post-deployment adaptation and fine-tuning of Deep Neural …
(MCUs) is a key step for post-deployment adaptation and fine-tuning of Deep Neural …