TinyML: Getting Started with Machine Learning on Microcontrollers
TinyML: Getting Started with Machine Learning on Microcontrollers 2026-05-31 — by Amer Thiab Since the rise of Deep Learning in the early 2010s, marked by marked by AlexNet’s 2012 ImageNet victory, most Machine Learning workloads have relied on powerful servers, GPUs, and cloud infrastructure to meet their computational and memory demands. As models became increasingly capable, they also became increasingly difficult to deploy on resource-constrained embedded devices. While cloud-based processing offers high computational capacity, its reliance on data transmission introduces latency, high operational costs, and absolute dependence on network connectivity. For simple applications like a smoke detector, a hearing aid,… Read More »TinyML: Getting Started with Machine Learning on Microcontrollers
