A review of TinyML

TL;DR

This paper reviews TinyML, an emerging field enabling machine learning on low-power embedded devices, highlighting its methodology, industrial applications, challenges, and future prospects.

Contribution

It provides a comprehensive overview of TinyML, detailing its methodology, benefits, obstacles, and potential future developments in edge computing.

Findings

TinyML enables deep neural networks on micro-controllers.

TinyML applications can improve industrial automation and IoT services.

Challenges include resource constraints and model optimization.

Abstract

In this current technological world, the application of machine learning is becoming ubiquitous. Incorporating machine learning algorithms on extremely low-power and inexpensive embedded devices at the edge level is now possible due to the combination of the Internet of Things (IoT) and edge computing. To estimate an outcome, traditional machine learning demands vast amounts of resources. The TinyML concept for embedded machine learning attempts to push such diversity from usual high-end approaches to low-end applications. TinyML is a rapidly expanding interdisciplinary topic at the convergence of machine learning, software, and hardware centered on deploying deep neural network models on embedded (micro-controller-driven) systems. TinyML will pave the way for novel edge-level services and applications that survive on distributed edge inferring and independent decision-making rather than server computation. In this paper, we explore TinyML's methodology, how TinyML can benefit a few specific industrial fields, its obstacles, and its future scope.