GPUs are widely used to accelerate the training of machine learning workloads. As modern
machine learning models become increasingly larger, they require a longer time to train …

GPU accelerators have become essential to the recent advance in computational power of
high-performance computing (HPC) systems. Current HPC systems' reaching an …

In recent years, more and more transistors have been integrated within the GPU, which has
resulted in steadily rising power consumption requirements. In this paper we present a …

Graphics Processing Units (GPUs) have revolutionized the computing landscape over the
past decade. However, the growing energy demands of data centres and computing …

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

With the widespread use of smart technologies, graphics processing unit (GPU) power-
optimization issues are becoming increasingly important. Many researchers have tried to …

Graphics Processing Units (GPUs) are rapidly dominating the accelerator space, as
illustrated by their wide-spread adoption in the data analytics and machine learning markets …

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

Power consumption poses a significant challenge in current and emerging graphics
processing unit (GPU) enabled high-performance computing systems. In modern GPUs …

Cloud inference systems have recently emerged as a solution to the ever-increasing
integration of AI-powered applications into the smart devices around us. The wide adoption …