Showing a promising future in improving resource utilization and accelerating training,
elastic deep learning training has been attracting more and more attention recently …

Learn in-situ is a growing trend for Edge AI. Training deep neural network (DNN) on edge
devices is challenging because both energy and memory are constrained. Low precision …

This paper investigates the problem of performing distributed deep learning (DDL) to train
machine learning (ML) models at the edge with resource-constrained embedded devices …

Distributed synchronized GPU training is commonly used for deep learning. The resource
constraint of using fixed GPUs makes large-scale deep learning training jobs suffer, and …

Cloud training platforms, such as Amazon Web Services and Huawei Cloud provide users
with computational resources to train their deep learning jobs. Elastic training is a service …

Deep Neural Networks (DNNs) have advanced the state-of-the-art in a variety of machine
learning tasks and are deployed in increasing numbers of products and services. However …

Data loading can dominate deep neural network training time on large-scale systems. We
present a comprehensive study on accelerating data loading performance in large-scale …

Deep Learning (DL) training platforms are built by interconnecting multiple DL accelerators
(eg, GPU/TPU) via fast, customized interconnects with 100s of gigabytes (GBs) of bandwidth …

Inference with trained machine learning models is now pos-sible with small computing
devices while only a few years ago it was run mostly in the cloud only. The recent technique …

Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce
the training time of deep learning models by using a cluster of GPU servers. While such …