Issue No. 05 - May (2018 vol. 51)
Nicholas D. Lane , University of Oxford
Pete Warden , Google
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.
embedded deep learning, machine learning, machine learning systems, deep model compression, mobile deep neural networks, embedded systems, mobile, mobile computing, pervasive computing, intelligent systems
N. D. Lane and P. Warden, "The Deep (Learning) Transformation of Mobile and Embedded Computing," in Computer, vol. 51, no. 5, pp. 12-16, 2018.