Atharv Gyan

Introduction In recent years, the convergence of machine learning (ML) and the Internet of Things (IoT) has given rise to Tiny Machine Learning (TinyML), a paradigm that enables on-device inference on resource-constrained microcontrollers and edge devices. TinyML shifts intelligence from centralized cloud servers to the very edge of networks, unlocking new possibilities in privacy, latency, and energy efficiency. This article provides a comprehensive, in-depth exploration of TinyML its origins, core frameworks, optimization techniques, real-world applications, challenges, and future directions designed as a standalone primer for developers, researchers, and technology enthusiasts. What Is TinyML? Historical Context and Definition TinyML is broadly defined as the practice of running ML models on microcontrollers and low-power embedded systems, typically operating in the milliwatt (mW) power range or below. Historically, ML inference required significant computational resources, relegat...