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 …

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …

Privacy protection has been an important concern with the great success of machine
learning. In this paper, it proposes a multi-party privacy preserving machine learning …

Deep learning workloads are common in today's production clusters due to the proliferation
of deep learning driven AI services (eg, speech recognition, machine translation). A deep …

Machine learning (ML) is widely used to derive useful information from large-scale data
(such as user activities, pictures, and videos) generated at increasingly rapid rates, all over …

PipeDream is a Deep Neural Network (DNN) training system for GPUs that parallelizes
computation by pipelining execution across multiple machines. Its pipeline parallel …

Deep Neural Network (DNN) models have continuously been growing in size in order to
improve the accuracy and quality of the models. Moreover, for training of large DNN models …

Extreme Classification comprises multi-class or multi-label prediction where there is a large
number of classes, and is increasingly relevant to many real-world applications such as text …

Embedding models have been an effective learning paradigm for high-dimensional data.
However, one open issue of embedding models is that their representations (latent factors) …

This paper presents Tofu, a system that partitions very large DNN models across multiple
GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow …