Researchers from the Karlsruhe Institute of Technology and the University of Patras have developed a new approach to tiny machine learning (tinyML) for printed electronics, optimizing machine learning models for ultra-low-power applications. This approach aims to enable efficient neural networks for wearables, implants, and other applications, while addressing the limitations of traditional silicon-based systems. The team's solution introduces 'sequential super-tinyML multi-layer perceptron circuits' for multi-sensory applications, achieving significant improvements in area efficiency and power consumption compared to existing methods.