The success of deep neural networks (DNNs) is heavily dependent on computational
resources. While DNNs are often employed on cloud servers, there is a growing need to …

The success of deep neural networks (DNNs) as an enabler of artificial intelligence (AI) is
heavily dependent on high computational resources. The increasing demands for …

The collaborative inference approach splits the Deep Neural Networks (DNNs) model into
two parts. It runs collaboratively on the end device and cloud server to minimize inference …

Recently, deep neural networks (DNNs) have been widely applied in mobile intelligent
applications. The inference for the DNNs is usually performed in the cloud. However, it leads …

Deep neural networks (DNNs) have been widely used in many intelligent applications such
as object recognition and automatic driving due to their superior performance in conducting …

Despite the soaring use of convolutional neural networks (CNNs) in mobile applications,
uniformly sustaining high-performance inference on mobile has been elusive due to the …

Deep neural networks (DNNs) have great potential to solve many real-world problems, but
they usually require an extensive amount of computation and memory. It is of great difficulty …

For highly distributed environments such as edge computing, collaborative learning
approaches eschew the dependence on a global, shared model, in favor of models tailored …

Deep neural network (DNN)-enabled edge intelligence has been widely adopted to support
a variety of smart applications because of its ability to preserve privacy and conserve …

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 …