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.

1. Renesas Electronics Corporation has introduced the Reality AI Explorer Tier, a free version of Reality AI Tools for AI and TinyML in industrial and automotive sectors. 2. The tier includes automated model construction, validation, and deployment modules, along with tutorials and email support. 3. It aims to provide a simplified evaluation environment for embedded real-time analytics, offering pre-built AI applications and seamless integration with the company's compute products.

1. Ceva has announced a neural network processing core, NPN32 and NPN64, designed for SoCs running TinyML models. 2. NPN32 features 32 int8 MACs and is optimized for voice, audio, object detection, and anomaly detection. 3. NPN64 offers 64 int8 MACs, providing double the performance with enhanced features for more complex AI tasks.