2 citations · 4 across the 3 of their papers we have counts for
3 papers
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…
nelli: a lightweight frontend for MLIR
Maksim Levental, Alok Kamatar, Ryan Chard +2
Multi-Level Intermediate Representation (MLIR) is a novel compiler infrastructure that aims to provide modular and extensible components to facilitate building domain specific comp…
OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Science
Maksim Levental, Arham Khan, Ryan Chard +3
In many experiment-driven scientific domains, such as high-energy physics, material science, and cosmology, high data rate experiments impose hard constraints on data acquisition s…