Tiny Machine Learning

Tiny Machine Learning (TinyML) focuses on deploying machine learning models onto resource-constrained microcontrollers, enabling intelligent functionalities in edge devices with limited power, memory, and processing capabilities. Current research emphasizes optimizing model architectures (like CNNs and Transformers) through techniques such as model compression, quantization, and pruning, alongside efficient algorithms for on-device training and inference, including reinforcement learning and federated learning approaches. This field is significant for its potential to power a wide range of resource-limited applications, from wearable health monitoring to industrial automation and environmental sensing, while also driving innovation in energy-efficient hardware and software co-design.