Deep Neural Networks have gained significant attraction due to their wide applicability in
different domains. DNN sizes and training samples are constantly growing, making training …

Distributed Deep Neural Network (DNN) training is a technique to reduce the training
overhead by distributing the training tasks into multiple accelerators, according to a …

Distributed training is a solution to reduce DNN training time by splitting the task across
multiple NPUs (eg, GPU/TPU). However, distributed training adds communication overhead …

Modern Deep Learning systems heavily rely on distributed training over high-performance
accelerator (eg, TPU, GPU)-based hardware platforms. Examples today include Google's …

Due to its many applications across various fields of research, engineering, and daily life,
deep learning has seen a surge in popularity. Therefore, larger and more expressive models …

Most work in the deep learning systems community has focused on faster inference, but
arriving at a trained model requires lengthy experiments. Accelerating training lets …

As model sizes in machine learning continue to scale, distributed training is necessary to
accommodate model weights within each device and to reduce training time. However, this …

Distributed training of Deep Neural Networks (DNNs) on High-Performance Computing
(HPC) systems is becoming increasingly common. HPC systems dedicated entirely or …

Modern Deep Learning (DL) models have grown to sizes requiring massive clusters of
specialized, high-end nodes to train. Designing such clusters to maximize both performance …

Deep neural networks (DNNs) have gained tremendous attractions as compelling solutions
for applications such as image classification, object detection, speech recognition, and so …