… activities, given different deep learning models and inference … deep learning inference, in
this paper we develop a mobile … on adapting deep neural networks to the mobile devices, we …

… of deep neural networks can be significantly improved by splitting the network between
the mobile device and cloud. This paper introduces a new deep learning architecture, called …

… performance of deep learning inference on edge devices are … as the integrity of the deep
learning model and framework. … inferencing on IoT and mobile devices. We implement and …

… In this article, we briefly introduce the basic principles associated with mobile device-based
mental health analysis, review the main system components, and highlight the conventional …

… Deep learning shows great promise in providing more intelligence to augmented reality (AR)
devices, but few AR apps use deep learning … Delivering deep learning to mobile devices via …

… on resource-constrained mobile devices, eg, model … mobile devices, and fill the gap
between the academic literature and industry products. ML/DL as cloud services Besides on-device …

… by many factors such as distance between mobile device and cloud, network bandwidth
and … of MobileDeepPill, a small-footprint mobile deep learning system that achieves state-of-the-…

… As such, FlexDNN allows developers with limited deep learning expertise to build efficient
DNN-based on-device video analytics applications with minimum effort. As illustrated in …

… As the objective of the research is to build a mobile application for image classification, mobile
devices used may not have the best camera quality, as such the CNN needed to train with …

… use of mobile devices, the vast diversity of mobile apps, and the … In this paper, we propose
a deep learning model for mobile app … Furthermore, the rapid spread of mobile devices has …