The Internet of Things (IoT) heralds the emergence of multitudes of computing-enabled networked everyday devices with sensing capabilities in homes, cars, workplaces, and on our persons, leading to ubiquitous smarter environments and smarter cyber-physical “things” The next natural step in this computing evolution is to develop the infrastructure needed for these computational things to collectively learn Advances in deep learning offer remarkable results but require significant computing resources The question is: how can we bring advantages of deep learning to the emerging world of embedded IoT devices? We discuss several core challenges in embedded and mobile deep learning, including effectiveness, efficiency, and reliability Recent solutions demonstrate the feasibility of building IoT applications that are powered by effective, efficient, and reliable deep learning models Evaluation results and experiences presented offer encouraging evidence of viability of deep learning for IoT

Shuochao Yao is a PhD candidate in Computer Science at University of Illinois at Urbana-Champaign, advised by Professor Tarek Abdelzaher. Previously, he was advised by Professor Xingbing Wang and graduated from SJTU in 2014. His research lies in the system efficiency, reliability, semi-supervision, and related applications of deep learning enabled IoT. He is the recipient of the SenSys Best Paper Award Nominee (2017) and the ICCPS Best Paper Award (2017). https://yscacaca.github.io/