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Found 7 papers, 1 papers with code
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries.
OpenHLS: High-Level Synthesis for Low-Latency Deep Neural Networks for Experimental Science
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 systems: collected data must either be indiscriminately stored for post-processing and analysis, thereby necessitating large storage capacity, or accurately filtered in real-time, thereby necessitating low-latency processing.
Memory Planning for Deep Neural Networks
We observe that such memory allocation patterns, in the context of multi-threading, are subject to high latencies, due to \texttt{mutex} contention in the system memory allocator.
Ultrafast Focus Detection for Automated Microscopy
Technological advancements in modern scientific instruments, such as scanning electron microscopes (SEMs), have significantly increased data acquisition rates and image resolutions enabling new questions to be explored; however, the resulting data volumes and velocities, combined with automated experiments, are quickly overwhelming scientists as there remain crucial steps that require human intervention, for example reviewing image focus.
Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics.
Comparing the costs of abstraction for DL frameworks
High level abstractions for implementing, training, and testing Deep Learning (DL) models abound.
Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis
Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in catalysis, energy materials, composites, and more.
