The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

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 state-of-the-art hardware platforms for training deep neural networks are moving from
traditional single precision (32-bit) computations towards 16 bits of precision-in large part …

Deep learning can achieve higher accuracy than traditional machine learning algorithms in
a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn …

Presently, deep learning models are an alternative solution for predicting solar energy
because of their accuracy. The present study reviews deep learning models for handling …

Most distributed machine learning systems nowadays, including TensorFlow and CNTK, are
built in a centralized fashion. One bottleneck of centralized algorithms lies on high …

Most commonly used distributed machine learning systems are either synchronous or
centralized asynchronous. Synchronous algorithms like AllReduce-SGD perform poorly in a …

Deep convolutional neural networks take GPU-days of computation to train on large data
sets. Pedestrian detection for self driving cars requires very low latency. Image recognition …

When training machine learning models using stochastic gradient descent (SGD) with a
large number of nodes or massive edge devices, the communication cost of synchronizing …

Large-scale machine learning training, in particular distributed stochastic gradient descent,
needs to be robust to inherent system variability such as node straggling and random …