Deep Neural Networks (DNNs) have shown significant advantages in many domains, such
as pattern recognition, prediction, and control optimization. The edge computing demand in …

Employing hardware accelerators to improve the performance and energy-efficiency of DNN
applications is on the rise. One challenge of using hardware accelerators, including the GPU …

Recently, a large number of convolutional neural network (CNN) inference services have
emerged on high-performance Graphic Processing Units (GPUs). However, GPUs are high …

The metaverse has recently gained increasing attention from the public. It builds up a virtual
world where we can live as a new role regardless of the role we play in the physical world …

Neural networks (NNs) have been used in a wide variety of artificial intelligence (AI)
applications, including speech recognition, image recognition, automatic robotics, and …

The proportion of machine learning (ML) inference in modern cloud workloads is rapidly
increasing, and graphic processing units (GPUs) are the most preferred computational …

GPU accelerators can deliver significant improvement for DNN processing; however, their
performance is limited by internal and external parameters. A well-known parameter that …

The success of convolutional neural networks (CNNs) has made low-latency inference
services on Graphic Processing Units (GPUs) a hot research topic. However, GPUs are …

Power consumption is one of the top limiting factors in high-performance computing systems
and data centers, and dynamic voltage and frequency scaling (DVFS) is an important …

The use of GPUs to accelerate DNN training and inference is already widely adopted,
allowing for a significant performance increase. However, this performance is usually …