TinyML: Deploying Machine Learning on Microcontrollers for IoT Applications

Abstract

The rapid proliferation of Internet of Things (IoT) devices has created an urgent need for intelligent data processing directly on resource-constrained hardware. Tiny Machine Learning (TinyML) addresses this challenge by enabling the deployment of machine learning models on microcontrollers and other low-power embedded systems with limited memory, processing power, and energy resources. This paper explores the fundamental concepts, techniques, and hardware platforms underpinning TinyML, emphasizing model compression methods such as quantization and pruning, alongside efficient neural architectures tailored for embedded environments. We highlight key applications of TinyML across diverse IoT domains including smart homes, wearable health monitoring, environmental sensing, and industrial automation. Despite its promise, TinyML faces significant challenges related to hardware constraints, energy efficiency, model accuracy trade-offs, and security. The paper further discusses emerging research directions such as ultra-low-power hardware advancements, federated and on-device incremental learning, and automated model optimization techniques. By bridging the gap between machine learning and embedded systems, TinyML paves the way for more responsive, privacy-preserving, and scalable IoT applications, marking a critical step toward truly intelligent edge computing.