Model partitioning is a promising technique for improving the efficiency of distributed
inference by executing partial deep neural network (DNN) models on edge servers (ESs) or …

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 …

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 …

Edge intelligence, as a prospective paradigm for accelerating DNN inference, is mostly
implemented by model partitioning which inevitably incurs the large transmission overhead …

Deep Neural Networks (DNN) have been widely used in a large number of application
scenarios. However, DNN models are generally both computation-intensive and memory …

Edge intelligence has emerged as a promising paradigm to accelerate DNN inference by
model partitioning, which is particularly useful for intelligent scenarios that demand high …

Deep learning technology has grown significantly in new application scenarios such as
smart cities and driverless vehicles, but its deployment needs to consume a lot of resources …

Deep Neural Networks (DNNs) are widely used to analyze the abundance of data collected
by massive Internet-of-Thing (IoT) devices. The traditional approaches usually send the data …

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 …

Recently, the notions of partitioning the Deep Neural Network (DNN) model over the multi-
level computing units and making a fast inference with the early-inference technique have …