Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. Due to
its unique features, the GPU continues to remain the most widely used accelerator for DL …

The large communication overhead has imposed a bottleneck on the performance of
distributed Stochastic Gradient Descent (SGD) for training deep neural networks. Previous …

High performance computing (HPC) clouds are becoming an alternative to on-premise
clusters for executing scientific applications and business analytics services. Most research …

Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Plant stress is one of major issues that cause significant economic loss for growers. The
labor-intensive conventional methods for identifying the stressed plants constrain their …

Applying machine learning techniques to the quickly growing data in science and industry
requires highly-scalable algorithms. Large datasets are most commonly processed" data …

The amalgamation of artificial intelligence with Internet of Things (AIoT) devices have seen a
rapid surge in growth, largely due to the effective implementation of deep neural network …

Abstract High-Performance Computing (HPC) research, from hardware and software to the
end applications, provides remarkable computing power to help scientists solve complex …

Deep learning frameworks have been widely deployed on GPU servers for deep learning
applications in both academia and industry. In training deep neural networks (DNNs), there …