TINYML: A cutting-edge technology revolutionizing machine learning in healthcare

Abstract

In the recent technological revolution, Machine learning (ML) models are hugely successful due to the availability of the resources like powerful servers with large storage capacities and multiple graphical processing units (GPUs). Eventually, this paved way for large neural networks running virtually on unlimited cloud resources. However, there has been a shift towards the need for creating machine learning models tailored for edge devices that facilitate data transmission between local networks and the cloud. The aim is to eliminate delays caused by sending data to remote servers for processing, which can degrade performance and hinder the user experience. Instead, machine learning models must operate locally on these edge devices. This demand has led to the development of Tiny Machine Learning (TinyML), a technology designed for devices with constraints in data, memory, processing power and connectivity. Thus, this transformative technology promises to revolutionize healthcare landscape, paving the way for a healthier and more affordable future for everyone. This paper explores the underlying principles, key features and potential applications of TinyML, as well as its future prospects in the healthcare sector.