Computational storage devices enable in-storage processing of data in place. These
devices contain 64-bit application processors and hardware accelerators that can help …

Cloud computing provides a powerful yet low-cost environment for distributed deep learning
workloads. However, training complex deep learning models often requires accessing large …

Deep neural networks (DNNs) are widely used in various AI application domains such as
computer vision, natural language processing, autonomous driving, and bioinformatics. As …

On large-scale high performance computing (HPC) systems, applications are provisioned
with aggregated resources to meet their peak demands for brief periods. This results in …

Machine learning algorithms have become a major tool in various applications. The high-
performance requirements on large-scale datasets pose a challenge for traditional von …

In this letter, we present deep partitioned training to accelerate computations involved in
training DNN models. This is the first work that partitions a DNN model across storage …

Deep Learning (DL) training requires efficient access to large collections of data, leading DL
frameworks to implement individual I/O optimizations to take full advantage of storage …

DNN models trained with very large datasets can perform rich deep learning tasks with high
accuracy. However, feeding huge volumes of training data exerts significant pressure on IO …

We present Monarch, a framework-agnostic storage middleware that transparently employs
storage tiering to accelerate Deep Learning (DL) training. It leverages existing storage tiers …

Cloud providers offer a variety of storage solutions for hosting data, both in price and in
performance. For Analytics and machine learning applications, object storage services are …