With the vigorous development of artificial intelligence (AI), intelligence applications based
on deep neural networks (DNNs) have changed people's lifestyles and production …

With the rapid development of Internet-of-Things (IoT) and the explosive advance of deep
learning, there is an urgent need to enable deep learning inference on IoT devices in Mobile …

Reducing the inference time of Deep Neural Networks (DNNs) is critical when running time
sensitive applications on mobile devices. Existing research has shown that partitioning a …

Deep Neural Networks (DNNs) have been widely deployed in mobile applications. DNN
inference latency is a critical metric to measure the service quality of those applications …

The quality of service (QoS) of intelligent applications on mobile devices heavily depends on
the inference speed of Deep Neural Network (DNN) models. Cooperative DNN inference …

We observe that accurate and fast tracking in Internet of Things (IoT) devices is still a
challenging problem. Several deep learning models have emerged which provide higher …

With the development of Internet of Things (IoT) and the advance of deep learning, there is
an urgent need to enable deep learning inference on IoT devices. To address the …

Abstract Deep Neural Network (DNN) models have been widely deployed in a variety of
applications. To achieve better performance, DNN models become more and more complex …

In recent times, advances in the technologies of Internet-of-Things (IoT) and Deep Neural
Networks (DNN) have significantly increased the accuracy and speed of a variety of smart …

Jie Wu 1 Employment History Page 1 Jie Wu Director, Center of Networked Computing
Laura H. Carnell Professor, Department of Computer and Information Sciences (CIS) …