Results 31 to 40 of about 2,597 (210)
TinyOL: TinyML with Online-Learning on Microcontrollers [PDF]
2021 International Joint Conference on Neural Networks (IJCNN), 2021 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 power, memory, and computation, TinyML has achieved significant advancement in the last few years.
Haoyu Ren +2 more
openaire +2 more sources
CoRR, 2021 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 (microcontroller powered) devices operating at extremely low power range (mW range and below).
openaire +2 more sources
Benchmarking TinyML Systems: Challenges and Direction
CoRR, 2020 6 pages, 1 figure, 3 ...
Banbury, Colby R. +16 more
openaire +3 more sources
TinyML based Deep Learning Model for Activity Detection [PDF]
, 2023 Our physical and emotional well-being are directly impacted by our body positions. In addition to promoting a confident, upright image, maintaining good body posture during various activities also ensures that our musculoskeletal system is properly ...
Gera, Bharath Mahesh +3 more
core +1 more source
An Ultra-low Power TinyML System for Real-time Visual Processing at Edge
, 2023 Tiny machine learning (TinyML), executing AI workloads on resource and power strictly restricted systems, is an important and challenging topic. This brief firstly presents an extremely tiny backbone to construct high efficiency CNN models for various ...
Lai, Rui +5 more
core +1 more source
Tạp chí Khoa họcThe convergence of artificial intelligence (AI) and wearables is ushering in a new paradigm of software development - AI- powered app creation - where AI is not just a feature but a software creation tool.
Nguyen Thi Dung*, Nguyen Thu Phuong, Doan Ngoc Phuong
doaj +1 more source
Widening Access to Applied Machine Learning with TinyML [PDF]
, 2021 Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations.
Agarwal, Anant +24 more
core +1 more source
Machine Learning for Microcontroller-Class Hardware -- A Review [PDF]
, 2022 The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct ...
Saha, Swapnil Sayan +2 more
core +3 more sources
On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
IEEE AccessAs technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats.
Fatemeh Dehrouyeh +3 more
doaj +1 more source
A Machine Learning-oriented Survey on Tiny Machine Learning
, 2023 The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures.
Capogrosso, Luigi +4 more
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