The wave of modernization around us has put the automotive industry on the brink of a
paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily …

Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …

At the confluence of two great paradigms such as Edge Computing and Artificial Intelligence,
Edge Intelligence arises. This new concept is about the smart exploitation of Edge …

Traditional deep neural network (DNN) training is executed with data parallelism, which
suffers from significant communication overheads and GPU memory consumption …

Distributing the inference of convolutional neural network (CNN) to multiple mobile devices
has been studied in recent years to achieve real-time inference without losing accuracy …

Given the widespread use of Convolutional Neural Networks (CNNs) in image classification
applications, cloud providers now routinely offer several GPU-equipped instances with …

Data parallelism and model parallelism are two typical parallel modes for distributed
machine learning (DML). Traditionally, DML mainly leverages data parallelism, which …

Convolutional neural networks (CNNs) are emerging as powerful tools for image processing
in important commercial applications. We focus on the important problem of improving the …

Distributed machine learning (DML) has become the common practice in industry, because
of the explosive volume of training data and the growing complexity of training model …

A system comprises compute nodes distributed over a network and configured to perform a
pipeline parallel process. The system also comprises an extended memory comprising a …