Research Area

Embedded Sytems, TinyML, EdgeAI, CNN, DQNN

• develop tiny machine learning (tinyML) algorithms and compressed deep neural networks (DNNs) in order to integrate such methods directly on the embedded devices (edgeAI) and micro-controller units (MCUs)
• develop deeply quantized learning algorithms (DQNN) and associated applications targeting MCUs
• develop artificial intelligence algorithms based on deep learning and computer vision for Advanced Driver Assistance Systems (ADAS).
• develop intelligent embedded systems for smart agriculture, based on deep learning techniques applied to images.

• Vehicle and pedestrian detection (VaPD) through image semantic segmentation or object detection neural networks on embedded systems
• Visual Simultaneous localization and mapping (SLAM) applications
• Plant diseases classification
• Anomaly detection in industry

Embedded Systems and Artificial Intelligence Lab

Laura Falaschetti

STMicroelectronics, System Research and Applications, Agrate Brianza