Mobile and embedded devices increasingly rely on deep neural networks to understand the world--a feat that would have overwhelmed their system resources only a few years ago. Further integration of machine learning and embedded/mobile systems will require additional breakthroughs of efficient learning algorithms that can function under fluctuating resource constraints, giving rise to a field that straddles computer architecture, software systems, and artificial intelligence. N. D. Lane and P. Warden, "The Deep (Learning) Transformation of Mobile and Embedded Computing," in Computer, vol.
New and innovative technologies enable a variety of instructional environments that help students overcome many traditional boundaries and constraints to learning. As the classroom becomes more of an abstraction than a physical space, educators and learners embrace a variety of pioneering tech-powered teaching and learning paradigms that will serve students well upon graduation.
Advances in embedded systems and communication technologies--along with the availability of low-cost sensors--have led to a pervasive presence of cyber-physical systems. However, several intelligent functionalities that are necessary to meet user and application demands are missing from current solutions. A homogeneous and integrated framework supporting intelligent mechanisms can fill this gap.