70 citations · 85 across the 10 of their papers we have counts for
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cs.LG2024★ 1 cited
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Nathaniel Hudson, J. Gregory Pauloski, Matt Baughman +13
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we…
cs.LG2022
Memory Planning for Deep Neural Networks
Maksim Levental
We study memory allocation patterns in DNNs during inference, in the context of large-scale systems. We observe that such memory allocation patterns, in the context of multi-thread…
cs.LG2020★ 2 cited
Comparing the costs of abstraction for DL frameworks
Maksim Levental, Elena Orlova
High level abstractions for implementing, training, and testing Deep Learning (DL) models abound. Such frameworks function primarily by abstracting away the implementation details…