Mobile devices have become an essential part of our daily lives. By virtue of both their increasing computing power and the recent progress made in AI, mobile devices evolved to …
Y Wang, J Wang, W Zhang, Y Zhan, S Guo… - Digital Communications …, 2022 - Elsevier
With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network …
Most of the research on deep neural networks so far has been focused on obtaining higher accuracy levels by building increasingly large and deep architectures. Training and …
Deep learning (DL) has demonstrated great performance in various applications on powerful computers and servers. Recently, with the advancement of more powerful mobile …
J Wang, B Cao, P Yu, L Sun, W Bao… - 2018 IEEE 38th …, 2018 - ieeexplore.ieee.org
Recent years have witnessed an explosive growth of mobile devices. Mobile devices are permeating every aspect of our daily lives. With the increasing usage of mobile devices and …
With the deep penetration of mobile devices, more and more mobile deep learning applications have been widely used in daily life. However, since deep learning tasks are …
Y Zhou, K Yang - 2022 IEEE 24th Int Conf on High Performance …, 2022 - ieeexplore.ieee.org
Deep learning (DL) has dramatically evolved and become one of the most successful machine learning techniques. A variety of DL-enabled applications have been widely …
Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud …
T Tan, G Cao - Proceedings of the 20th International Conference on …, 2021 - dl.acm.org
Many Deep Neural Network (DNN) based applications have been developed and run on mobile devices. Although these advanced DNN models can provide better results, they also …