Machine learning has numerous important applications in intelligent systems within many areas, like automotive, avionics, robotics, health-care, well-being, and security. The recent progress in Artificial Intelligence (AI), and particularly in Deep Learning (DL) \/ Machine Learning (ML), has dramatically improved the state-of-the-art in object detection, classification and recognition, natural language processing, games, medical imaging, etc. However, the complexity of DL-networks for many practical applications can be huge, and their processing may demand a high computing effort and excessive energy consumption. This can become a gigantic challenge when considering embedded inference implementation for Smart Cyber Physical Systems (like autonomous vehicles and robotics) and Internet-of-Things (like healthcare-IoT and predictive maintenance for Industry 4.0). Moreover, even training of such complex DL-networks over massive data sets is triggering new avenues in training accelerator design. In DSD 2022, we plan to organize several oral sessions on embedded deep learning\/AI and related research, as well as to have invited speeches, and a poster session.<\/p>\n\n\n
We welcome submissions related to advanced applications, architectures, design methods and tools, and system software for AI, ML and DL, especially related (but not limited) to the following topics:<\/p>\n