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 …

Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

Neural networks dominate the modern machine learning landscape, but their training and
success still suffer from sensitivity to empirical choices of hyperparameters such as model …

We introduce Gandiva, a new cluster scheduling framework that utilizes domain-specific
knowledge to improve latency and efficiency of training deep learning models in a GPU …

Modern distributed machine learning (ML) training workloads benefit significantly from
leveraging GPUs. However, significant contention ensues when multiple such workloads are …

Video analytics applications use edge compute servers for processing videos. Compressed
models that are deployed on the edge servers for inference suffer from data drift where the …

Deep learning has made significant achievements in many application areas. To train and
test models more efficiently, enterprise developers submit and run their deep learning …

Model compression is significant for the wide adoption of Recurrent Neural Networks
(RNNs) in both user devices possessing limited resources and business clusters requiring …

Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

We introduce Quiver, an informed storage cache for deep learning training (DLT) jobs in a
cluster of GPUs. Quiver employs domain-specific intelligence within the caching layer, to …