Filter News
Area of Research
- (-) Supercomputing (21)
- Advanced Manufacturing (6)
- Biology and Environment (6)
- Building Technologies (1)
- Clean Energy (46)
- Computer Science (2)
- Fusion and Fission (1)
- Fusion Energy (1)
- Isotopes (3)
- Materials (55)
- Materials for Computing (3)
- National Security (5)
- Neutron Science (16)
- Nuclear Science and Technology (11)
- Quantum information Science (1)
News Topics
- (-) 3-D Printing/Advanced Manufacturing (3)
- (-) Artificial Intelligence (6)
- (-) Bioenergy (3)
- (-) Frontier (1)
- (-) Grid (2)
- (-) Isotopes (1)
- (-) Materials Science (7)
- (-) Physics (3)
- Big Data (8)
- Biology (1)
- Biomedical (8)
- Chemical Sciences (1)
- Climate Change (1)
- Computer Science (34)
- Coronavirus (8)
- Critical Materials (1)
- Cybersecurity (1)
- Decarbonization (1)
- Energy Storage (1)
- Environment (4)
- Exascale Computing (2)
- Fusion (1)
- High-Performance Computing (2)
- Machine Learning (4)
- Materials (2)
- Mathematics (1)
- Microscopy (2)
- Molten Salt (1)
- Nanotechnology (4)
- National Security (1)
- Neutron Science (8)
- Nuclear Energy (1)
- Polymers (2)
- Quantum Science (9)
- Summit (15)
- Sustainable Energy (4)
- Transportation (3)
Media Contacts
![ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system. ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.](/sites/default/files/styles/list_page_thumbnail/public/news/images/RAvENNA%20release%20pic.png?itok=2bDpK5Mo)
A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the