Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are
placed in various fields. Many of these devices are based on machine learning (ML) models …

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 …

The rapid growth in miniaturization of low-power embedded devices and advancement in
the optimization of machine learning (ML) algorithms have opened up a new prospect of the …

Tiny Machine Learning (TML) is a novel research area aiming at designing and developing
Machine Learning (ML) techniques meant to be executed on Embedded Systems and …

Machine Learning (ML) on the edge is key to enabling a new breed of IoT and autonomous
system applications. The departure from the traditional cloud-centric architecture means that …

Tiny Machine Learning (TinyML), a rapidly evolving edge computing concept that links
embedded systems (hardware and software) and machine learning, with the purpose of …

The rapid emergence of low-power embedded devices and modern machine learning (ML)
algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks …

In a few years, the world will be populated by billions of connected devices that will be
placed in our homes, cities, vehicles, and industries. Devices with limited resources will …

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 …

Machine learning has traditionally been solely performed on servers and high-performance
machines. However, advances in chip technology have given us miniature libraries that fit in …