Deep neural networks (DNNs) have been increasingly deployed on and integrated with
edge devices, such as mobile phones, drones, robots and wearables. Compared to cloud …

Deep neural networks (DNNs) have been increasingly deployed on and integrated with
edge devices, such as mobile phones, drones, robots and wearables. To run DNN inference …

Deep neural networks (DNNs) have been increasingly used in recent years to achieve
higher inference accuracy; however, implementing deeper networks in edge-computing …

Deep neural networks (DNNs) have succeeded in many different perception tasks, eg,
computer vision, natural language processing, reinforcement learning, etc. The high …

In recent years, deep neural networks (DNNs) have witnessed a booming of artificial
intelligence Internet of Things applications with stringent demands across high accuracy and …

Deep neural network (DNN) has demonstrated promising performance in various machine
learning tasks. Due to the privacy issue and the unpredictable transmission latency, inferring …

Deep learning is currently widely used in a variety of applications, including computer vision
and natural language processing. End devices, such as smartphones and Internet-of-Things …

Optimising deep learning inference across edge devices and optimisation targets such as
inference time, memory footprint and power consumption is a key challenge due to the …

As the backbone technology of machine learning, deep neural networks (DNNs) have have
quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices …

Deep Learning approaches based on Convolutional Neural Networks (CNNs) are
extensively utilized and very successful in a wide range of application areas, including …