Cloud computing is a commercial and economic paradigm that has gained traction since
2006 and is presently the most significant technology in IT sector. From the notion of cloud …

The progress of some AI paradigms such as deep learning is said to be linked to an
exponential growth in the number of parameters. There are many studies corroborating …

Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this
often comes at the cost of training models for extensive periods on specialized hardware …

Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services
in both the research community and industry. When operating a datacenter, optimization of …

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 …

For many decades, progress in computing hardware has been closely associated with
CMOS logic density, performance, and cost. As such, slowdown in 2-D scaling, frequency …

The rapid growth of memory and computation requirements of large language models
(LLMs) has outpaced the development of hardware, hindering people who lack large-scale …

Deep learning has become widely used in complex AI applications. Yet, training a deep
neural network (DNNs) model requires a considerable amount of calculations, long running …

We empirically evaluate an undervolting technique, ie, underscaling the circuit supply
voltage below the nominal level, to improve the power-efficiency of Convolutional Neural …

Energy saving is a crucial mission for data center providers. Among many services, DNN
training and inference are significant contributors to energy consumption. This work focuses …