The Deep (Learning) Transformation of Mobile and Embedded Computing

IEEE Computer

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.

Sustaining Moore’s Law with 3D Chips

IEEE Computer

Rather than continue the expensive and time-consuming quest for transistor replacement, the authors argue that 3D chips coupled with new computer architectures can keep Moore's law on its traditional scaling path.

Advances in Learning Technologies

IEEE Computer

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.