Special Issue on TinyML

Vijay Janapa Reddi, Boris Murmann

Abstract: This IEEE Micro special issue on tiny machine learning (TinyML) explores cutting-edge research on optimizing machine learning models for highly resource-constrained devices like microcontrollers and embedded systems. The articles cover techniques across the full TinyML stack, including efficient neural network design, on-device learning, model compression, hardware–software co-design, and specialized applications. These selected works showcase techniques to enable increasingly sophisticated intelligence on low-power, memory-constrained edge devices. They provide valuable insights to overcome challenges in deploying performant yet compact TinyML solutions that can perceive, reason, and interact intelligently, even at the very edge.