Y Ran, X Zhou, P Lin, Y Wen, R Deng - arXiv preprint arXiv:1912.07383, 2019 - arxiv.org
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 …
P Mattson, C Cheng, G Diamos… - Proceedings of …, 2020 - proceedings.mlsys.org
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 …
S Mittal, S Vaishay - Journal of Systems Architecture, 2019 - Elsevier
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 …