Deep learning (DL) has demonstrated great performance in various applications on
powerful computers and servers. Recently, with the advancement of more powerful mobile …

Abstract Generative Adversarial Networks (GANs) have achieved huge success in
generating high-fidelity images, however, they suffer from low efficiency due to tremendous …

Deep neural networks (DNNs) have been successfully applied to a great variety of
applications, ranging from small IoT devices to large scale services in a data center. In order …

Livestock welfare and management could be greatly enhanced by the replacement of
branding or ear tagging with less invasive visual biometric identification methods. Biometric …

With the wide adoption of AI applications, there is a pressing need of enabling real-time
neural network (NN) inference on small embedded devices, but deploying NNs and …

CPU is a powerful, pervasive, and indispensable platform for running deep learning (DL)
workloads in systems ranging from mobile to extreme-end servers. In this article, we present …

The memristor crossbar array has emerged as an intrinsically suitable matrix computation
and low-power acceleration framework for DNN applications. Many techniques such as …

The high computation and memory storage of large deep neural networks (DNNs) models
pose intensive challenges to the conventional Von-Neumann architecture, incurring sub …

Mobile fitness applications provide the opportunity to show users real-time feedback on their
current fitness activity. For such applications, it is essential to accurately track the user's …

Recent research demonstrates the potential of touch dynamics as a usable and privacy-
preserving scheme for smartphone authentication. Most existing approaches rely on …