Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …

Serverless computing is a new pay-per-use cloud service paradigm that automates resource
scaling for stateless functions and can potentially facilitate bursty machine learning serving …

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 …

Consumer electronics such as mobile phones, wearable devices, and vehicle electronics
use many intelligent applications such as voice commands, machine translation, and face …

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

Motivated by the popularity of edge intelligence, DNN services have been widely deployed
at the edge, posing significant performance pressure on edge servers. How to improve the …

Over the last years, the ever-growing number of Machine Learning (ML) and Artificial
Intelligence (AI) applications deployed in the Cloud has led to high demands on the …

With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

Deep learning models are increasingly used for end-user applications, supporting both
novel features such as facial recognition, and traditional features, eg web search. To …