Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields,
ranging from speech processing, image classification to drug discovery. This is driven by the …

This paper provides a comprehensive literature review on Predictive Maintenance (PdM)
with emphasis on system architectures, purposes and approaches. In industry, any outages …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Abstract Machine learning is experiencing an explosion of software and hardware solutions,
and needs industry-standard performance benchmarks to drive design and enable …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

We present FFCV, a library for easy, fast, resource-efficient training of machine learning
models. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from …

The rise of deep-learning (DL) has been fuelled by the improvements in accelerators. Due to
its unique features, the GPU continues to remain the most widely used accelerator for DL …

Federated Learning is an emerging direction in distributed machine learning that en-ables
jointly training a model without sharing the data. Since the data is distributed across many …

This paper proposes optical network interconnects as a key enabler for building high-
bandwidth ML training clusters with strong scaling properties. Our design, called SiP-ML …

We present CASSINI, a network-aware job scheduler for machine learning (ML) clusters.
CASSINI introduces a novel geometric abstraction to consider the communication pattern of …