Recently, there has been a burgeoning popularity in the deployment of deep learning vision
applications upon terminal devices. However, as the number of layers in deep neural …

With the widespread use of heterogeneous computing platforms in the AI field, neural
network models on heterogeneous computing platforms are experiencing problems such as …

Deep Neural Network (DNN) is a promising technology for Internet of Visual Things (IoVT)
devices to extract their visual information from unstructured data. However, it is hard to …

The execution of deep neural network (DNN) inference jobs on edge devices has become
increasingly popular. Multiple of such inference models can concurrently analyse the on …

Commercial AI accelerators are gaining popularity because of their high energy efficiency
for the inference of deep neural networks (DNNs). How to benchmark them remains hot …

Myriad applications of Deep Neural Networks (DNN) and the race for better accuracy have
paved the way for the development of more computationally intensive network architectures …

Real-time visual computing applications running Deep Neural Networks (DNN) are
becoming popular for mission-critical use cases such as, disaster response, tactical …

Deep Neural Networks (DNNs) have achieved excellent performance in intelligent
applications. Nevertheless, it is elusive for devices with limited resources to support …

The rapid development of deep neural networks (DNNs) has provided a strong foundation
for the popularization of intelligent applications. However, the limited computing power of IoT …

Deep neural networks (DNNs) are becoming the core components of many applications
running on edge devices, especially for real time image-based analysis. Increasingly, multi …