Deep neural network (DNN) inference with low delay and high accuracy requirements is
usually computation intensive. The collaboration among mobile devices and the network …

With the increasing proliferation of Internet-of-Things (IoT) devices, there is a growing trend
towards distributing the power of deep learning (DL) among edge devices rather than …

Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to
overwhelming explosions of mobile applications, by offloading compute-intensive tasks to …

As the key technology of artificial intelligence (AI), Deep Neural Networks (DNNs) have been
widely used in mobile applications, such as video analytics in autonomous driving …

Implementing intelligent mobile applications on IoT devices with DNN technology has
become an inevitable trend. Due to the limitations of the size of DNN model deployed onto …

Deep neural networks (DNNs) sustain high performance in today's data processing
applications. DNN inference is resource-intensive thus is difficult to fit into a mobile device …

Recent years witnessed an increasing research attention in deploying deep learning models
on edge devices for inference. Due to limited capabilities and power constraints, it may be …

Distributing the inference of convolutional neural network (CNN) to multiple mobile devices
has been studied in recent years to achieve real-time inference without losing accuracy …

Collaborative inference is a promising method for balancing the limited computational power
of Internet of Things (IoT) devices with the huge computational demands of convolutional …

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 …